glmmTMB/0000755000176200001440000000000013616106055011554 5ustar liggesusersglmmTMB/NAMESPACE0000644000176200001440000000754113616061432013001 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method(VarCorr,glmmTMB) S3method(anova,glmmTMB) S3method(as.data.frame,ranef.glmmTMB) S3method(coef,glmmTMB) S3method(confint,glmmTMB) S3method(confint,profile.glmmTMB) S3method(df.residual,glmmTMB) S3method(extractAIC,glmmTMB) S3method(family,glmmTMB) S3method(fitted,glmmTMB) S3method(fixef,glmmTMB) S3method(formula,glmmTMB) S3method(getME,glmmTMB) S3method(isLMM,glmmTMB) S3method(logLik,glmmTMB) S3method(model.frame,glmmTMB) S3method(model.matrix,glmmTMB) S3method(nobs,glmmTMB) S3method(predict,glmmTMB) S3method(print,VarCorr.glmmTMB) S3method(print,coef.glmmTMB) S3method(print,fixef.glmmTMB) S3method(print,glmmTMB) S3method(print,ranef.glmmTMB) S3method(print,summary.glmmTMB) S3method(print,vcov.glmmTMB) S3method(profile,glmmTMB) S3method(ranef,glmmTMB) S3method(refit,glmmTMB) S3method(residuals,glmmTMB) S3method(sigma,glmmTMB) S3method(simulate,glmmTMB) S3method(summary,glmmTMB) S3method(terms,glmmTMB) S3method(vcov,glmmTMB) S3method(weights,glmmTMB) export(VarCorr) export(addForm) export(beta_family) export(betabinomial) export(compois) export(dropHead) export(extractForm) export(fixef) export(genpois) export(getCapabilities) export(getGrpVar) export(getME) export(getReStruc) export(get_cor) export(glmmTMB) export(glmmTMBControl) export(inForm) export(nbinom1) export(nbinom2) export(noSpecials) export(numFactor) export(parseNumLevels) export(ranef) export(sigma) export(splitForm) export(truncated_compois) export(truncated_genpois) export(truncated_nbinom1) export(truncated_nbinom2) export(truncated_poisson) export(tweedie) export(ziGamma) if(getRversion() >= "3.6.0") { S3method(car::Anova, glmmTMB) } else { export(Anova.glmmTMB) } if(getRversion() >= "3.6.0") { S3method(effects::Effect, glmmTMB) } else { export(Effect.glmmTMB) } if(getRversion()>='3.3.0') importFrom(stats, sigma) else importFrom(lme4,sigma) importFrom(Matrix,Cholesky) importFrom(Matrix,solve) importFrom(Matrix,t) importFrom(TMB,MakeADFun) importFrom(TMB,openmp) importFrom(TMB,sdreport) importFrom(TMB,tmbprofile) importFrom(lme4,.prt.VC) importFrom(lme4,.prt.aictab) importFrom(lme4,.prt.call) importFrom(lme4,.prt.family) importFrom(lme4,.prt.grps) importFrom(lme4,.prt.resids) importFrom(lme4,findbars) importFrom(lme4,getME) importFrom(lme4,isLMM) importFrom(lme4,mkReTrms) importFrom(lme4,nobars) importFrom(lme4,refit) importFrom(lme4,subbars) importFrom(methods,as) importFrom(methods,is) importFrom(methods,new) importFrom(nlme,VarCorr) importFrom(nlme,fixef) importFrom(nlme,ranef) importFrom(splines,backSpline) importFrom(splines,interpSpline) importFrom(stats,"contrasts<-") importFrom(stats,AIC) importFrom(stats,BIC) importFrom(stats,anova) importFrom(stats,as.formula) importFrom(stats,binomial) importFrom(stats,complete.cases) importFrom(stats,confint) importFrom(stats,contrasts) importFrom(stats,delete.response) importFrom(stats,df.residual) importFrom(stats,dist) importFrom(stats,family) importFrom(stats,fitted) importFrom(stats,formula) importFrom(stats,gaussian) importFrom(stats,getCall) importFrom(stats,logLik) importFrom(stats,make.link) importFrom(stats,model.frame) importFrom(stats,model.matrix) importFrom(stats,model.response) importFrom(stats,model.weights) importFrom(stats,na.fail) importFrom(stats,na.pass) importFrom(stats,napredict) importFrom(stats,nlminb) importFrom(stats,nobs) importFrom(stats,optimHess) importFrom(stats,pchisq) importFrom(stats,plogis) importFrom(stats,pnorm) importFrom(stats,poisson) importFrom(stats,predict) importFrom(stats,printCoefmat) importFrom(stats,profile) importFrom(stats,qchisq) importFrom(stats,qnorm) importFrom(stats,residuals) importFrom(stats,runif) importFrom(stats,setNames) importFrom(stats,simulate) importFrom(stats,terms) importFrom(stats,update) importFrom(stats,var) importFrom(stats,vcov) importFrom(stats,weights) importFrom(stats,xtabs) importFrom(utils,head) useDynLib(glmmTMB) glmmTMB/data/0000755000176200001440000000000013614324717012472 5ustar liggesusersglmmTMB/data/epil2.rda0000644000176200001440000000563213614324717014203 0ustar liggesusers{pT7JI Q( V.a vC  (jS)F c"Z(b)Z< #bw_SNwg|}9wlnH!$=JA4$ H^q8u>NE G5?gOW|@87~7tptͣ\h,ݷ\ur,nI"_NI9|sWzmꤟ8KfuL@Y~l꽃r~CE~9ҙ_zrsGkޒ #H1bRޯy|n~|:=Ez@jHxʇǷk1= iW\!J#}߲x~tt&ۖϏ9|d ?l *x$9<k&7IӞv\w9vQn8x~K7 ms'#?qAsXڪTh,+%߆/ }˂/n(gSoZgwWo7mª뷎<ɾCΐf3gk3um?.>{] HŇ3?:'v|zzzzzz}]~mqZW}}. 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Ӥeh}+u %OSiXϨJPbs/)Fy^ K[W?4#,P'AaqqIpA>QCȐO-q [wr4>wVaj8.U4ԝS'~<9hLQ%9yؕ:BL{\T?dzrZlI8Q. B%߫P5ϫӃ"]aT? ܊>S03|NBNL_q}(8r?w}^>R>DI oip\}~8T𺜏ZNϪ ?*ʏ~y~|qeI_΃Q%>(5](5P:>'OL=P:zOtt~hw=1Coeo#K U6)wE_7~lGo* }͟y%z눜XwCNj=?:z-/WSOkߣ|}_}^u6u)}.} \item{digits}{number of significant digits to use.} \item{comp}{a string specifying the component to format and print.} \item{formatter}{a \code{\link{function}}.} \item{...}{optional further arguments, passed the next \code{\link{print}} method.} } \description{ Printing The Variance and Correlation Parameters of a \code{glmmTMB} } glmmTMB/man/getGrpVar.Rd0000644000176200001440000000123513614324717014525 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/glmmTMB.R \name{getGrpVar} \alias{getGrpVar} \title{Get Grouping Variable} \usage{ getGrpVar(x) } \arguments{ \item{x}{"flist" object; a data frame of factors including an \code{assign} attribute matching columns to random effect terms} } \value{ character vector of grouping variables } \description{ Extract grouping variables for random effect terms from a factor list } \examples{ data(cbpp,package="lme4") cbpp$obs <- factor(seq(nrow(cbpp))) rt <- lme4::glFormula(cbind(size,incidence-size)~(1|herd)+(1|obs), data=cbpp,family=binomial)$reTrms getGrpVar(rt$flist) } \keyword{internal} glmmTMB/man/sigma.glmmTMB.Rd0000644000176200001440000000761013614324717015225 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/VarCorr.R \name{sigma.glmmTMB} \alias{sigma.glmmTMB} \alias{sigma} \title{Extract residual standard deviation or dispersion parameter} \usage{ \method{sigma}{glmmTMB}(object, ...) } \arguments{ \item{object}{a \dQuote{glmmTMB} fitted object} \item{\dots}{(ignored; for method compatibility)} } \description{ For Gaussian models, \code{sigma} returns the value of the residual standard deviation; for other families, it returns the dispersion parameter, \emph{however it is defined for that particular family}. See details for each family below. } \details{ The value returned varies by family: \describe{ \item{gaussian}{returns the \emph{maximum likelihood} estimate of the standard deviation (i.e., smaller than the results of \code{sigma(lm(...))} by a factor of (n-1)/n)} \item{nbinom1}{returns an overdispersion parameter (usually denoted \eqn{\alpha}{alpha} as in Hardin and Hilbe (2007)): such that the variance equals \eqn{\mu(1+\alpha)}{mu(1+alpha)}.} \item{nbinom2}{returns an overdispersion parameter (usually denoted \eqn{\theta}{theta} or \eqn{k}); in contrast to most other families, larger \eqn{\theta}{theta} corresponds to a \emph{lower} variance which is \eqn{\mu(1+\mu/\theta)}{mu(1+mu/theta)}.} \item{Gamma}{Internally, glmmTMB fits Gamma responses by fitting a mean and a shape parameter; sigma is estimated as (1/sqrt(shape)), which will typically be close (but not identical to) that estimated by \code{stats:::sigma.default}, which uses sqrt(deviance/df.residual)} \item{beta}{returns the value of \eqn{\phi}{phi}, where the conditional variance is \eqn{\mu(1-\mu)/(1+\phi)}{mu*(1-mu)/(1+phi)} (i.e., increasing \eqn{\phi}{phi} decreases the variance.) This parameterization follows Ferrari and Cribari-Neto (2004) (and the \code{betareg} package):} \item{betabinomial}{This family uses the same parameterization (governing the Beta distribution that underlies the binomial probabilities) as \code{beta}.} \item{genpois}{returns the index of dispersion \eqn{\phi^2}{phi^2}, where the variance is \eqn{\mu\phi^2}{mu*phi^2} (Consul & Famoye 1992)} \item{compois}{returns the value of \eqn{1/\nu}{1/nu}, When \eqn{\nu=1}{nu=1}, compois is equivalent to the Poisson distribution. There is no closed form equation for the variance, but it is approximately undersidpersed when \eqn{1/\nu <1}{1/nu <1} and approximately oversidpersed when \eqn{1/\nu >1}{1/nu>1}. In this implementation, \eqn{\mu}{mu} is exactly the mean (Huang 2017), which differs from the COMPoissonReg package (Sellers & Lotze 2015).} \item{tweedie}{returns the value of \eqn{\phi}{phi}, where the variance is \eqn{\phi\mu^p}{phi*mu^p}. The value of \eqn{p} can be extracted using the internal function \code{glmmTMB:::.tweedie_power}.} } The most commonly used GLM families (\code{binomial}, \code{poisson}) have fixed dispersion parameters which are internally ignored. } \references{ \itemize{ \item Consul PC, and Famoye F (1992). "Generalized Poisson regression model. Communications in Statistics: Theory and Methods" 21:89–109. \item Ferrari SLP, Cribari-Neto F (2004). "Beta Regression for Modelling Rates and Proportions." \emph{J. Appl. Stat.} 31(7), 799-815. \item Hardin JW & Hilbe JM (2007). "Generalized linear models and extensions." Stata press. \item Huang A (2017). "Mean-parametrized Conway–Maxwell–Poisson regression models for dispersed counts. " \emph{Statistical Modelling} 17(6), 1-22. \item Sellers K & Lotze T (2015). "COMPoissonReg: Conway-Maxwell Poisson (COM-Poisson) Regression". R package version 0.3.5. https://CRAN.R-project.org/package=COMPoissonReg } } glmmTMB/man/dot-collectDuplicates.Rd0000644000176200001440000000044113614324717017051 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/glmmTMB.R \name{.collectDuplicates} \alias{.collectDuplicates} \title{collapse duplicated observations} \usage{ .collectDuplicates(data.tmb) } \description{ collapse duplicated observations } \keyword{internal} glmmTMB/man/VarCorr.glmmTMB.Rd0000644000176200001440000000255313614324717015504 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/VarCorr.R \name{VarCorr.glmmTMB} \alias{VarCorr.glmmTMB} \alias{VarCorr} \title{Extract variance and correlation components} \usage{ \method{VarCorr}{glmmTMB}(x, sigma = 1, ...) } \arguments{ \item{x}{a fitted \code{glmmTMB} model} \item{sigma}{residual standard deviation (usually set automatically from internal information)} \item{extra}{arguments (for consistency with generic method)} } \description{ Extract variance and correlation components } \details{ For an unstructured variance-covariance matrix, the internal parameters are structured as follows: the first n parameters are the log-standard-deviations, while the remaining n(n-1)/2 parameters are the elements of the Cholesky factor of the correlation matrix, filled in column-wise order (see the \href{http://kaskr.github.io/adcomp/classUNSTRUCTURED__CORR__t.html}{TMB documentation} for further details). } \examples{ ## Comparing variance-covariance matrix with manual computation data("sleepstudy",package="lme4") fm4 <- glmmTMB(Reaction ~ Days + (Days|Subject), sleepstudy) VarCorr(fm4)[[c("cond","Subject")]] ## hand calculation pars <- getME(fm4,"theta") ## construct cholesky factor L <- diag(2) L[lower.tri(L)] <- pars[-(1:2)] C <- crossprod(L) diag(C) <- 1 sdvec <- exp(pars[1:2]) (V <- outer(sdvec,sdvec) * C) } \keyword{internal} glmmTMB/man/weights.glmmTMB.Rd0000644000176200001440000000200113614324717015564 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/methods.R \name{weights.glmmTMB} \alias{weights.glmmTMB} \title{Extract weights from a glmmTMB object} \usage{ \method{weights}{glmmTMB}(object, type = "prior", ...) } \arguments{ \item{object}{a fitted \code{glmmTMB} object} \item{type}{weights type} \item{...}{additional arguments (not used; for methods compatibility)} } \description{ Extract weights from a glmmTMB object } \details{ At present only explicitly specified \emph{prior weights} (i.e., weights specified in the \code{weights} argument) can be extracted from a fitted model. \itemize{ \item Unlike other GLM-type models such as \code{\link{glm}} or \code{\link[lme4]{glmer}}, \code{weights()} does not currently return the total number of trials when binomial responses are specified as a two-column matrix. \item Since \code{glmmTMB} does not fit models via iteratively weighted least squares, \code{working weights} (see \code{\link[stats]{weights.glm}}) are unavailable. } } glmmTMB/man/getME.glmmTMB.Rd0000644000176200001440000000117413614324717015125 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/methods.R \name{getME.glmmTMB} \alias{getME.glmmTMB} \alias{getME} \title{Extract or Get Generalize Components from a Fitted Mixed Effects Model} \usage{ \method{getME}{glmmTMB}(object, name = c("X", "Xzi", "Z", "Zzi", "Xd", "theta", "beta"), ...) } \arguments{ \item{object}{a fitted \code{glmmTMB} object} \item{name}{of the component to be retrieved} \item{\dots}{ignored, for method compatibility} } \description{ Extract or Get Generalize Components from a Fitted Mixed Effects Model } \seealso{ \code{\link[lme4]{getME}} Get generic and re-export: } glmmTMB/man/formula.glmmTMB.Rd0000644000176200001440000000122113614324717015562 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/methods.R \name{formula.glmmTMB} \alias{formula.glmmTMB} \title{Extract the formula of a glmmTMB object} \usage{ \method{formula}{glmmTMB}(x, fixed.only = FALSE, component = c("cond", "zi", "disp"), ...) } \arguments{ \item{x}{a \code{glmmTMB} object} \item{fixed.only}{(logical) drop random effects, returning only the fixed-effect component of the formula?} \item{component}{formula for which component of the model to return (conditional, zero-inflation, or dispersion)} \item{...}{unused, for generic consistency} } \description{ Extract the formula of a glmmTMB object } glmmTMB/man/epil2.Rd0000644000176200001440000000341613614324717013642 0ustar liggesusers\name{epil2} \title{Seizure Counts for Epileptics - Extended} \alias{epil2} \docType{data} \description{ Extended version of the \code{epil} dataset of the \pkg{MASS} package. The three transformed variables \code{Visit}, \code{Base}, and \code{Age} used by Booth et al. (2003) have been added to \code{epil}. } \usage{epil2} \format{ A data frame with 236 observations on the following 12 variables: \describe{ \item{\code{y}}{an integer vector.} \item{\code{trt}}{a factor with levels \code{"placebo"} and \code{"progabide"}.} \item{\code{base}}{an integer vector.} \item{\code{age}}{an integer vector.} \item{\code{V4}}{an integer vector.} \item{\code{subject}}{an integer vector.} \item{\code{period}}{an integer vector.} \item{\code{lbase}}{a numeric vector.} \item{\code{lage}}{a numeric vector.} \item{Visit}{\code{(rep(1:4,59) - 2.5) / 5}.} \item{Base}{\code{log(base/4)}.} \item{Age}{\code{log(age)}.} } } \references{ Booth, J.G., G. Casella, H. Friedl, and J.P. Hobert. (2003) Negative binomial loglinear mixed models. \emph{Statistical Modelling} \bold{3}, 179--191. } \examples{ \donttest{ epil2$subject <- factor(epil2$subject) op <- options(digits=3) (fm <- glmmTMB(y ~ Base*trt + Age + Visit + (Visit|subject), data=epil2, family=nbinom2)) meths <- methods(class = class(fm)) if((Rv <- getRversion()) > "3.1.3") { (funs <- attr(meths, "info")[, "generic"]) for(F in funs[is.na(match(funs, "getME"))]) { cat(sprintf("\%s:\n-----\n", F)) r <- tryCatch( get(F)(fm), error=identity) if (inherits(r, "error")) cat("** Error:", r$message,"\n") else tryCatch( print(r) ) cat(sprintf("---end{\%s}--------------\n\n", F)) } } options(op) } } \keyword{datasets} glmmTMB/man/getReStruc.Rd0000644000176200001440000000235613614324717014720 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/glmmTMB.R \name{getReStruc} \alias{getReStruc} \title{Calculate random effect structure Calculates number of random effects, number of parameters, block size and number of blocks. Mostly for internal use.} \usage{ getReStruc(reTrms, ss = NULL) } \arguments{ \item{reTrms}{random-effects terms list} \item{ss}{a character string indicating a valid covariance structure. Must be one of \code{names(glmmTMB:::.valid_covstruct)}; default is to use an unstructured variance-covariance matrix (\code{"us"}) for all blocks).} } \value{ a list \item{blockNumTheta}{number of variance covariance parameters per term} \item{blockSize}{size (dimension) of one block} \item{blockReps}{number of times the blocks are repeated (levels)} \item{covCode}{structure code} } \description{ Calculate random effect structure Calculates number of random effects, number of parameters, block size and number of blocks. Mostly for internal use. } \examples{ data(sleepstudy, package="lme4") rt <- lme4::lFormula(Reaction~Days+(1|Subject)+(0+Days|Subject), sleepstudy)$reTrms rt2 <- lme4::lFormula(Reaction~Days+(Days|Subject), sleepstudy)$reTrms getReStruc(rt) } glmmTMB/man/bootmer_methods.Rd0000644000176200001440000000267613614324717016030 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/methods.R \name{isLMM.glmmTMB} \alias{isLMM.glmmTMB} \alias{refit.glmmTMB} \title{support methods for parametric bootstrapping} \usage{ \method{isLMM}{glmmTMB}(object) \method{refit}{glmmTMB}(object, newresp, ...) } \arguments{ \item{object}{a fitted glmmTMB object} \item{newresp}{a new response vector} \item{...}{additional arguments (for generic consistency; ignored)} } \description{ see \code{\link[lme4]{refit}} and \code{\link[lme4]{isLMM}} for details } \details{ These methods are still somewhat experimental (check your results carefully!), but they should allow parametric bootstrapping. They work by copying and replacing the original response column in the data frame passed to \code{glmmTMB}, so they will only work properly if (1) the data frame is still available in the environment and (2) the response variable is specified as a single symbol (e.g. \code{proportion} or a two-column matrix constructed on the fly with \code{cbind()}. Untested with binomial models where the response is specified as a factor. } \examples{ if (requireNamespace("lme4")) { \dontrun{ fm1 <- glmmTMB(count~mined+(1|spp), ziformula=~mined, data=Salamanders, family=nbinom1) b1 <- lme4::bootMer(fm1, FUN=function(x) fixef(x)$zi, nsim=20, .progress="txt") if (requireNamespace("boot")) { boot.ci(b1,type="perc") } } } } glmmTMB/man/Salamanders.Rd0000644000176200001440000000326413614324717015062 0ustar liggesusers\name{Salamanders} \title{Repeated counts of salamanders in streams} \alias{Salamanders} \docType{data} \description{ A data set containing counts of salamanders with site covariates and sampling covariates. Each of 23 sites was sampled 4 times. When using this data set, please cite Price et al. (2016) as well as the Dryad data package (Price et al. 2015). } \usage{data(Salamanders)} \format{ A data frame with 644 observations on the following 10 variables: \describe{ \item{site}{name of a location where repeated samples were taken} \item{mined}{factor indicating whether the site was affected by mountain top removal coal mining} \item{cover}{amount of cover objects in the stream (scaled)} \item{sample}{repeated sample} \item{DOP}{Days since precipitation (scaled)} \item{Wtemp}{water temperature (scaled)} \item{DOY}{day of year (scaled)} \item{spp}{abbreviated species name, possibly also life stage} \item{count}{number of salamanders observed} } } \references{ Price SJ, Muncy BL, Bonner SJ, Drayer AN, Barton CD (2016) Effects of mountaintop removal mining and valley filling on the occupancy and abundance of stream salamanders. \emph{Journal of Applied Ecology} \bold{53} 459--468. \url{http://dx.doi.org/10.1111/1365-2664.12585} Price SJ, Muncy BL, Bonner SJ, Drayer AN, Barton CD (2015) Data from: Effects of mountaintop removal mining and valley filling on the occupancy and abundance of stream salamanders. \emph{Dryad Digital Repository}. \url{http://dx.doi.org/10.5061/dryad.5m8f6} } \examples{ require("glmmTMB") data(Salamanders) \donttest{ zipm3 = glmmTMB(count~spp * mined + (1|site), zi=~spp * mined, Salamanders, family="poisson") } } \keyword{datasets} glmmTMB/man/nbinom2.Rd0000644000176200001440000001024613614324717014172 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/family.R \name{nbinom2} \alias{nbinom2} \alias{family_glmmTMB} \alias{nbinom1} \alias{compois} \alias{truncated_compois} \alias{genpois} \alias{truncated_genpois} \alias{truncated_poisson} \alias{truncated_nbinom2} \alias{truncated_nbinom1} \alias{beta_family} \alias{betabinomial} \alias{tweedie} \alias{ziGamma} \title{Family functions for glmmTMB} \usage{ nbinom2(link = "log") nbinom1(link = "log") compois(link = "log") truncated_compois(link = "log") genpois(link = "log") truncated_genpois(link = "log") truncated_poisson(link = "log") truncated_nbinom2(link = "log") truncated_nbinom1(link = "log") beta_family(link = "logit") betabinomial(link = "logit") tweedie(link = "log") ziGamma(link = "inverse") } \arguments{ \item{link}{(character) link function for the conditional mean ("log", "logit", "probit", "inverse", "cloglog", "identity", or "sqrt")} } \value{ returns a list with (at least) components \item{family}{length-1 character vector giving the family name} \item{link}{length-1 character vector specifying the link function} \item{variance}{a function of either 1 (mean) or 2 (mean and dispersion parameter) arguments giving a value proportional to the predicted variance (scaled by \code{sigma(.)}) } } \description{ Family functions for glmmTMB } \details{ If specified, the dispersion model uses a log link. Denoting the variance as \eqn{V}, the dispersion parameter as \eqn{\phi=\exp(\eta)}{phi=exp(eta)} (where \eqn{\eta}{eta} is the linear predictor from the dispersion model), and the predicted mean as \eqn{\mu}{mu}: \describe{ \item{gaussian}{(from base R): constant \eqn{V=\phi}{V=phi}} \item{Gamma}{(from base R) phi is the shape parameter. \eqn{V=\mu\phi}{V=mu*phi}} \item{ziGamma}{a modified version of \code{Gamma} that skips checks for zero values, allowing it to be used to fit hurdle-Gamma models} \item{nbinom2}{Negative binomial distribution: quadratic parameterization (Hardin & Hilbe 2007). \eqn{V=\mu(1+\mu/\phi) = \mu+\mu^2/\phi}{V=mu*(1+mu/phi) = mu+mu^2/phi}.} \item{nbinom1}{Negative binomial distribution: linear parameterization (Hardin & Hilbe 2007). \eqn{V=\mu(1+\phi)}{V=mu*(1+phi)}} \item{compois}{Conway-Maxwell Poisson distribution: parameterized with the exact mean (Huang 2017), which differs from the parameterization used in the \pkg{COMPoissonReg} package (Sellers & Shmueli 2010, Sellers & Lotze 2015). \eqn{V=\mu\phi}{V=mu*phi}.} \item{genpois}{Generalized Poisson distribution (Consul & Famoye 1992). \eqn{V=\mu\exp(\eta)}{V=mu*exp(eta)}. (Note that Consul & Famoye (1992) define \eqn{\phi}{phi} differently.)} \item{beta}{Beta distribution: parameterization of Ferrari and Cribari-Neto (2004) and the \pkg{betareg} package (Cribari-Neto and Zeileis 2010); \eqn{V=\mu(1-\mu)\phi}{V=mu*(1-mu)*phi}} \item{betabinomial}{Beta-binomial distribution: parameterized according to Morris (1997). \eqn{V=\mu(1-\mu)(n(\phi+n)/(\phi+1))}{V=mu*(1-mu)*(n*(phi+n)/(phi+1))}} \item{tweedie}{Tweedie distribution: \eqn{V=\phi\mu^p}{V=phi*mu^p}. The power parameter is restricted to the interval \eqn{1=5))}. The \code{optimizer} argument can be any optimization (minimizing) function, provided that: \itemize{ \item the first three arguments, in order, are the starting values, objective function, and gradient function; \item it also takes a \code{control} argument; \item it returns a list with elements (at least) \code{convergence} (0 if convergence is successful) and \code{message} } } \examples{ ## fit with default (nlminb) and alternative (optim/BFGS) optimizer m1 <- glmmTMB(count~ mined, family=poisson, data=Salamanders) m1B <- update(m1, control=glmmTMBControl(optimizer=optim, optArgs=list(method="BFGS"))) ## estimates are *nearly* identical: all.equal(fixef(m1), fixef(m1B)) } glmmTMB/man/getCapabilities.Rd0000644000176200001440000000155213614324717015717 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/family.R \name{getCapabilities} \alias{getCapabilities} \title{List model options that glmmTMB knows about} \usage{ getCapabilities(what = "all", check = FALSE) } \arguments{ \item{what}{(character) which type of model structure to report on ("all","family","link","covstruct")} \item{check}{(logical) do brute-force checking to test whether families are really implemented (only available for \code{what="family"})} } \value{ if \code{check==FALSE}, returns a vector of the names (or a list of name vectors) of allowable entries; if \code{check==TRUE}, returns a logical vector of working families } \description{ List model options that glmmTMB knows about } \note{ these are all the options that are \emph{defined} internally; they have not necessarily all been \emph{implemented} (FIXME!) } glmmTMB/man/glmmTMB.Rd0000644000176200001440000002050013614324717014117 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/glmmTMB.R \name{glmmTMB} \alias{glmmTMB} \title{Fit Models with TMB} \usage{ glmmTMB( formula, data = NULL, family = gaussian(), ziformula = ~0, dispformula = ~1, weights = NULL, offset = NULL, contrasts = NULL, na.action = na.fail, se = TRUE, verbose = FALSE, doFit = TRUE, control = glmmTMBControl(), REML = FALSE, start = NULL, map = NULL ) } \arguments{ \item{formula}{combined fixed and random effects formula, following lme4 syntax.} \item{data}{optional data frame containing model variables.} \item{family}{a family function, a character string naming a family function, or the result of a call to a family function (variance/link function) information. See \code{\link{family}} for a generic discussion of families or \code{\link{family_glmmTMB}} for details of \code{glmmTMB}-specific families.} \item{ziformula}{a \emph{one-sided} (i.e., no response variable) formula for zero-inflation combining fixed and random effects: the default \code{~0} specifies no zero-inflation. Specifying \code{~.} sets the zero-inflation formula identical to the right-hand side of \code{formula} (i.e., the conditional effects formula); terms can also be added or subtracted. \strong{When using \code{~.} as the zero-inflation formula in models where the conditional effects formula contains an offset term, the offset term will automatically be dropped}. The zero-inflation model uses a logit link.} \item{dispformula}{a \emph{one-sided} formula for dispersion containing only fixed effects: the default \code{~1} specifies the standard dispersion given any family. The argument is ignored for families that do not have a dispersion parameter. For an explanation of the dispersion parameter for each family, see \code{\link{sigma}}. The dispersion model uses a log link. In Gaussian mixed models, \code{dispformula=~0} fixes the residual variance to be 0 (actually a small non-zero value: at present it is set to \code{sqrt(.Machine$double.eps)}), forcing variance into the random effects.} \item{weights}{weights, as in \code{glm}. Not automatically scaled to have sum 1.} \item{offset}{offset for conditional model (only).} \item{contrasts}{an optional list, e.g., \code{list(fac1="contr.sum")}. See the \code{contrasts.arg} of \code{\link{model.matrix.default}}.} \item{na.action}{how to handle missing values, see \code{\link{na.action}} and \code{\link{model.frame}}. From \code{\link{lm}}: \dQuote{The default is set by the \code{\link{na.action}} setting of \code{\link{options}}, and is \code{\link{na.fail}} if that is unset. The \sQuote{factory-fresh} default is \code{\link{na.omit}}.}} \item{se}{whether to return standard errors.} \item{verbose}{whether progress indication should be printed to the console.} \item{doFit}{whether to fit the full model, or (if FALSE) return the preprocessed data and parameter objects, without fitting the model.} \item{control}{control parameters, see \code{\link{glmmTMBControl}}.} \item{REML}{whether to use REML estimation rather than maximum likelihood.} \item{start}{starting values, expressed as a list with possible components \code{beta}, \code{betazi}, \code{betad} (fixed-effect parameters for conditional, zero-inflation, dispersion models); \code{b}, \code{bzi} (conditional modes for conditional and zero-inflation models); \code{theta}, \code{thetazi} (random-effect parameters, on the standard deviation/Cholesky scale, for conditional and z-i models); \code{thetaf} (extra family parameters, e.g., shape for Tweedie models).} \item{map}{a list specifying which parameter values should be fixed to a constant value rather than estimated. \code{map} should be a named list containing factors corresponding to a subset of the internal parameter names (see \code{start} parameter). Distinct factor values are fitted as separate parameter values, \code{NA} values are held fixed: e.g., \code{map=list(beta=factor(c(1,2,3,NA)))} would fit the first three fixed-effect parameters of the conditional model and fix the fourth parameter to its starting value. In general, users will probably want to use \code{start} to specify non-default starting values for fixed parameters. See \code{\link[TMB]{MakeADFun}} for more details.} } \description{ Fit a generalized linear mixed model (GLMM) using Template Model Builder (TMB). } \details{ Binomial models with more than one trial (i.e., not binary/Bernoulli) can either be specified in the form \code{prob ~ ..., weights = N}, or in the more typical two-column matrix \code{cbind(successes,failures)~...} form. Behavior of \code{REML=TRUE} for Gaussian responses matches \code{lme4::lmer}. It may also be useful in some cases with non-Gaussian responses (Millar 2011). Simulations should be done first to verify. Because the \code{\link{df.residual}} method for \code{glmmTMB} currently counts the dispersion parameter, one would need to multiply by \code{sqrt(nobs(fit) / (1+df.residual(fit)))} when comparing with \code{lm}. By default, vector-valued random effects are fitted with unstructured (general positive definite) variance-covariance matrices. Structured variance-covariance matrices can be specified in the form \code{struc(terms|group)}, where \code{struc} is one of \itemize{ \item \code{diag} (diagonal, heterogeneous variance) \item \code{ar1} (autoregressive order-1, homogeneous variance) \item \code{cs} (compound symmetric, heterogeneous variance) \item \code{ou} (* Ornstein-Uhlenbeck, homogeneous variance) \item \code{exp} (* exponential autocorrelation) \item \code{gau} (* Gaussian autocorrelation) \item \code{mat} (* Matérn process correlation) \item \code{toep} (* Toeplitz) } Structures marked with * are experimental/untested. For backward compatibility, the \code{family} argument can also be specified as a list comprising the name of the distribution and the link function (e.g. \code{list(family="binomial", link="logit")}). However, \strong{this alternative is now deprecated}; it produces a warning and will be removed at some point in the future. Furthermore, certain capabilities such as Pearson residuals or predictions on the data scale will only be possible if components such as \code{variance} and \code{linkfun} are present, see \code{\link{family}}. } \note{ For more information about the \pkg{glmmTMB} package, see Brooks et al. (2017) and the \code{vignette(package="glmmTMB")} collection. For the underlying \pkg{TMB} package that performs the model estimation, see Kristensen et al. (2016). } \examples{ (m1 <- glmmTMB(count ~ mined + (1|site), zi=~mined, family=poisson, data=Salamanders)) summary(m1) \donttest{ ## Zero-inflated negative binomial model (m2 <- glmmTMB(count ~ spp + mined + (1|site), zi=~spp + mined, family=nbinom2, data=Salamanders)) ## Hurdle Poisson model (m3 <- glmmTMB(count ~ spp + mined + (1|site), zi=~spp + mined, family=truncated_poisson, data=Salamanders)) ## Binomial model data(cbpp, package="lme4") (bovine <- glmmTMB(cbind(incidence, size-incidence) ~ period + (1|herd), family=binomial, data=cbpp)) ## Dispersion model sim1 <- function(nfac=40, nt=100, facsd=0.1, tsd=0.15, mu=0, residsd=1) { dat <- expand.grid(fac=factor(letters[1:nfac]), t=1:nt) n <- nrow(dat) dat$REfac <- rnorm(nfac, sd=facsd)[dat$fac] dat$REt <- rnorm(nt, sd=tsd)[dat$t] dat$x <- rnorm(n, mean=mu, sd=residsd) + dat$REfac + dat$REt dat } set.seed(101) d1 <- sim1(mu=100, residsd=10) d2 <- sim1(mu=200, residsd=5) d1$sd <- "ten" d2$sd <- "five" dat <- rbind(d1, d2) m0 <- glmmTMB(x ~ sd + (1|t), dispformula=~sd, data=dat) fixef(m0)$disp c(log(5^2), log(10^2)-log(5^2)) # expected dispersion model coefficients } ## Using 'map' to fix random-effects SD to 10 m1_map <- update(m1, map=list(theta=factor(NA)), start=list(theta=log(10))) VarCorr(m1_map) } \references{ Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Mächler, M. and Bolker, B. M. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. \emph{The R Journal}, \bold{9}(2), 378--400. Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H. and Bell, B. (2016). TMB: Automatic differentiation and Laplace approximation. \emph{Journal of Statistical Software}, \bold{70}, 1--21. Millar, R. B. (2011). \emph{Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB.} Wiley, New York. } glmmTMB/man/formFuns.Rd0000644000176200001440000000240113614324717014417 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{inForm} \alias{inForm} \alias{extractForm} \alias{dropHead} \alias{drop.special2} \title{test formula: does it contain a particular element?} \usage{ inForm(form, value) extractForm(term, value) dropHead(term, value) drop.special2(x, value = quote(offset), preserve = NULL) } \arguments{ \item{value}{term to remove from formula} \item{term}{expression/formula} \item{x}{formula} \item{preserve}{(integer) retain the specified occurrence of "value"} } \value{ a list of expressions } \description{ test formula: does it contain a particular element? extract terms with a given head from an expression/formula return a formula/expression with a given value stripped, where it occurs as the head of a term drop terms matching a particular value from an expression } \examples{ inForm(z~.,quote(.)) inForm(z~y,quote(.)) inForm(z~a+b+c,quote(c)) inForm(z~a+b+(d+e),quote(c)) f <- ~ a + offset(x) f2 <- z ~ a inForm(f,quote(offset)) inForm(f2,quote(offset)) extractForm(~a+offset(b),quote(offset)) extractForm(~c,quote(offset)) extractForm(~a+offset(b)+offset(c),quote(offset)) dropHead(~a+offset(b),quote(offset)) dropHead(~a+poly(x+z,3)+offset(b),quote(offset)) } \keyword{internal} glmmTMB/man/expandAllGrpVar.Rd0000644000176200001440000000110713614324717015654 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{expandAllGrpVar} \alias{expandAllGrpVar} \title{expand interactions/combinations of grouping variables} \usage{ expandAllGrpVar(bb) } \arguments{ \item{bb}{a list of naked grouping variables, i.e. 1 | f} } \description{ Modeled after lme4:::expandSlash, by Doug Bates } \examples{ ff <- glmmTMB:::fbx(y~1+(x|f/g)) glmmTMB:::expandAllGrpVar(ff) glmmTMB:::expandAllGrpVar(quote(1|(f/g)/h)) glmmTMB:::expandAllGrpVar(quote(1|f/g/h)) glmmTMB:::expandAllGrpVar(quote(1|f*g)) } \keyword{internal} glmmTMB/man/splitForm.Rd0000644000176200001440000000443313614324717014606 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{addForm} \alias{addForm} \alias{splitForm} \alias{noSpecials} \title{Combine right-hand sides of an arbitrary number of formulas} \usage{ addForm(...) splitForm( formula, defaultTerm = "us", allowFixedOnly = TRUE, allowNoSpecials = TRUE, debug = FALSE ) noSpecials(term, delete = TRUE, debug = FALSE) } \arguments{ \item{...}{arguments to pass through to \code{addForm0}} \item{formula}{a formula containing special random effect terms} \item{defaultTerm}{default type for non-special RE terms} \item{allowFixedOnly}{(logical) are formulas with no RE terms OK?} \item{allowNoSpecials}{(logical) are formulas with only standard RE terms OK?} \item{debug}{debugging mode (print stuff)?} \item{term}{language object} } \value{ a list containing elements \code{fixedFormula}; \code{reTrmFormulas} list of \code{x | g} formulas for each term; \code{reTrmAddArgs} list of function+additional arguments, i.e. \code{list()} (non-special), \code{foo()} (no additional arguments), \code{foo(addArgs)} (additional arguments); \code{reTrmClasses} (vector of special functions/classes, as character) } \description{ Parse a formula into fixed formula and random effect terms, treating 'special' terms (of the form foo(x|g[,m])) appropriately } \details{ Taken from Steve Walker's lme4ord, ultimately from the flexLambda branch of lme4 . Mostly for internal use. } \examples{ splitForm(~x+y) ## no specials or RE splitForm(~x+y+(f|g)) ## no specials splitForm(~x+y+diag(f|g)) ## one special splitForm(~x+y+(diag(f|g))) ## 'hidden' special splitForm(~x+y+(f|g)+cs(1|g)) ## combination splitForm(~x+y+(1|f/g)) ## 'slash'; term splitForm(~x+y+(1|f/g/h)) ## 'slash'; term splitForm(~x+y+(1|(f/g)/h)) ## 'slash'; term splitForm(~x+y+(f|g)+cs(1|g)+cs(a|b,stuff)) ## complex special splitForm(~(((x+y)))) ## lots of parentheses splitForm(~1+rr(f|g,n=2)) noSpecials(y~1+us(1|f)) noSpecials(y~1+us(1|f),delete=FALSE) noSpecials(y~us(1|f)) noSpecials(y~us+1) ## should *not* delete unless head of a function } \author{ Steve Walker } \keyword{internal} glmmTMB/man/vcov.glmmTMB.Rd0000644000176200001440000000147313614324717015103 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/methods.R \name{vcov.glmmTMB} \alias{vcov.glmmTMB} \title{Calculate Variance-Covariance Matrix for a Fitted glmmTMB model} \usage{ \method{vcov}{glmmTMB}(object, full = FALSE, ...) } \arguments{ \item{object}{a \dQuote{glmmTMB} fit} \item{full}{return a full variance-covariance matrix?} \item{\dots}{ignored, for method compatibility} } \value{ By default (\code{full==FALSE}), a list of separate variance-covariance matrices for each model component (conditional, zero-inflation, dispersion). If \code{full==TRUE}, a single square variance-covariance matrix for \emph{all} top-level model parameters (conditional, dispersion, and variance-covariance parameters) } \description{ Calculate Variance-Covariance Matrix for a Fitted glmmTMB model } glmmTMB/man/fbx.Rd0000644000176200001440000000165513614324717013411 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{fbx} \alias{fbx} \title{(f)ind (b)ars e(x)tended: recursive} \usage{ fbx(term, debug = FALSE, specials = character(0), default.special = "us") } \arguments{ \item{term}{a formula or piece of a formula} \item{debug}{(logical) debug?} \item{specials}{list of special terms} \item{default.special}{character: special to use for parenthesized terms - i.e. random effects terms with unspecified structure 1. atom (not a call or an expression): NULL 2. special, i.e. foo(...) where "foo" is in specials: return term 3. parenthesized term: \emph{if} the head of the head is | (i.e. it is of the form (xx|gg), then convert it to the default special type; we won't allow pathological cases like ((xx|gg)) ... [can we detect them?]} } \description{ (f)ind (b)ars e(x)tended: recursive } \examples{ splitForm(quote(us(x,n=2))) } \keyword{internal} glmmTMB/man/confint.glmmTMB.Rd0000644000176200001440000000641613614324717015570 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/methods.R \name{confint.glmmTMB} \alias{confint.glmmTMB} \title{Calculate confidence intervals} \usage{ \method{confint}{glmmTMB}( object, parm = NULL, level = 0.95, method = c("wald", "Wald", "profile", "uniroot"), component = c("all", "cond", "zi", "other"), estimate = TRUE, parallel = c("no", "multicore", "snow"), ncpus = getOption("profile.ncpus", 1L), cl = NULL, full = FALSE, ... ) } \arguments{ \item{object}{\code{glmmTMB} fitted object.} \item{parm}{which parameters to profile, specified \itemize{ \item by index (position) [\emph{after} component selection for \code{confint}, if any] \item by name (matching the row/column names of \code{vcov(object,full=TRUE)}) \item as \code{"theta_"} (random-effects variance-covariance parameters), \code{"beta_"} (conditional and zero-inflation parameters), or \code{"disp_"} or \code{"sigma"} (dispersion parameters) } Parameter indexing by number may give unusual results when some parameters have been fixed using the \code{map} argument: please report surprises to the package maintainers.} \item{level}{Confidence level.} \item{method}{'wald', 'profile', or 'uniroot': see Details function)} \item{component}{Which of the three components 'cond', 'zi' or 'other' to select. Default is to select 'all'.} \item{estimate}{(logical) add a third column with estimate ?} \item{parallel}{method (if any) for parallel computation} \item{ncpus}{number of CPUs/cores to use for parallel computation} \item{cl}{cluster to use for parallel computation} \item{full}{CIs for all parameters (including dispersion) ?} \item{...}{arguments may be passed to \code{\link{profile.merMod}} or \code{\link[TMB]{tmbroot}}} } \description{ Calculate confidence intervals } \details{ Available methods are \describe{ \item{"wald"}{These intervals are based on the standard errors calculated for parameters on the scale of their internal parameterization depending on the family. Derived quantities such as standard deviation parameters and dispersion parameters are back-transformed. It follows that confidence intervals for these derived quantities are typically asymmetric.} \item{"profile"}{This method computes a likelihood profile for the specified parameter(s) using \code{profile.glmmTMB}; fits a spline function to each half of the profile; and inverts the function to find the specified confidence interval.} \item{"uniroot"}{This method uses the \code{\link{uniroot}} function to find critical values of one-dimensional profile functions for each specified parameter.} } At present, "wald" returns confidence intervals for variance parameters on the standard deviation/correlation scale, while "profile" and "uniroot" report them on the underlying ("theta") scale: for each random effect, the first set of parameter values are standard deviations on the log scale, while remaining parameters represent correlations on the scaled Cholesky scale (see the } \examples{ data(sleepstudy, package="lme4") model <- glmmTMB(Reaction ~ Days + (1|Subject), sleepstudy) model2 <- glmmTMB(Reaction ~ Days + (1|Subject), sleepstudy, dispformula= ~I(Days>8)) confint(model) ## Wald/delta-method CIs confint(model,parm="theta_") ## Wald/delta-method CIs confint(model,parm=1,method="profile") } glmmTMB/man/simulate.glmmTMB.Rd0000644000176200001440000000162413614324717015747 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/methods.R \name{simulate.glmmTMB} \alias{simulate.glmmTMB} \title{Simulate from a glmmTMB fitted model} \usage{ \method{simulate}{glmmTMB}(object, nsim = 1, seed = NULL, ...) } \arguments{ \item{object}{glmmTMB fitted model} \item{nsim}{number of response lists to simulate. Defaults to 1.} \item{seed}{random number seed} \item{...}{extra arguments} } \value{ returns a list of vectors. The list has length \code{nsim}. Each simulated vector of observations is the same size as the vector of response variables in the original data set. In the binomial family case each simulation is a two-column matrix with success/failure. } \description{ Simulate from a glmmTMB fitted model } \details{ Random effects are also simulated from their estimated distribution. Currently, it is not possible to condition on estimated random effects. } glmmTMB/man/get_cor.Rd0000644000176200001440000000223113614324717014243 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{get_cor} \alias{get_cor} \title{translate vector of correlation parameters to correlation values} \usage{ get_cor(theta) } \arguments{ \item{theta}{vector of internal correlation parameters} } \value{ a vector of correlation values } \description{ translate vector of correlation parameters to correlation values } \details{ This function follows the definition at \url{http://kaskr.github.io/adcomp/classUNSTRUCTURED__CORR__t.html}: if \eqn{L} is the lower-triangular matrix with 1 on the diagonal and the correlation parameters in the lower triangle, then the correlation matrix is defined as \eqn{\Sigma = D^{-1/2} L L^\top D^{-1/2}}{Sigma = sqrt(D) L L' sqrt(D)}, where \eqn{D = \textrm{diag}(L L^\top)}{D = diag(L L')}. For a single correlation parameter \eqn{\theta_0}{theta0}, this works out to \eqn{\rho = \theta_0/\sqrt{1+\theta_0^2}}{rho = theta0/sqrt(1+theta0^2)}. The function returns the elements of the lower triangle of the correlation matrix, in column-major order. } \examples{ th0 <- 0.5 stopifnot(all.equal(get_cor(th0),th0/sqrt(1+th0^2))) get_cor(c(0.5,0.2,0.5)) } glmmTMB/man/fixef.Rd0000644000176200001440000000224613614324717013730 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/methods.R \docType{methods} \name{fixef} \alias{fixef} \alias{fixef.glmmTMB} \title{Extract fixed-effects estimates} \usage{ \method{fixef}{glmmTMB}(object, ...) } \arguments{ \item{object}{any fitted model object from which fixed effects estimates can be extracted.} \item{\dots}{optional additional arguments. Currently none are used in any methods.} } \value{ an object of class \code{fixef.glmmTMB} comprising a list of components (\code{cond}, \code{zi}, \code{disp}), each containing a (possibly zero-length) numeric vector of coefficients } \description{ Extract Fixed Effects } \details{ Extract fixed effects from a fitted \code{glmmTMB} model. The print method for \code{fixef.glmmTMB} object \emph{only displays non-trivial components}: in particular, the dispersion parameter estimate is not printed for models with a single (intercept) dispersion parameter (see examples) } \examples{ data(sleepstudy, package = "lme4") fm1 <- glmmTMB(Reaction ~ Days, sleepstudy) (f1 <- fixef(fm1)) f1$cond ## show full coefficients, including dispersion parameter unlist(f1) print.default(f1) } \keyword{models} glmmTMB/man/downstream_methods.Rd0000644000176200001440000000524513616054060016530 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Anova.R, R/effects.R, R/emmeans.R \name{Anova.glmmTMB} \alias{Anova.glmmTMB} \alias{Effect.glmmTMB} \alias{recover_data.glmmTMB} \alias{emm_basis.glmmTMB} \alias{downstream_methods} \title{Downstream methods for glmmTMB objects} \usage{ Anova.glmmTMB( mod, type = c("II", "III", 2, 3), test.statistic = c("Chisq", "F"), component = "cond", vcov. = vcov(mod)[[component]], singular.ok, ... ) Effect.glmmTMB(focal.predictors, mod, ...) recover_data.glmmTMB(object, ...) emm_basis.glmmTMB(object, trms, xlev, grid, component = "cond", ...) } \arguments{ \item{mod}{a glmmTMB model} \item{type}{type of test, \code{"II"}, \code{"III"}, \code{2}, or \code{3}. Roman numerals are equivalent to the corresponding Arabic numerals. See \code{\link[car]{Anova}} for details.} \item{test.statistic}{unused: only valid choice is "Chisq" (i.e., Wald chi-squared test)} \item{component}{which component of the model to compute emmeans for (conditional ("cond"), zero-inflation ("zi"), or dispersion ("disp"))} \item{vcov.}{variance-covariance matrix (usually extracted automatically)} \item{singular.ok}{OK to do ANOVA with singular models (unused) ?} \item{\dots}{Additional parameters that may be supported by the method.} \item{focal.predictors}{a character vector of one or more predictors in the model in any order.} \item{object}{a glmmTMB model} \item{trms}{The \code{terms} component of \code{object} (typically with the response deleted, e.g. via \code{\link{delete.response}}} \item{xlev}{Named list of factor levels (\emph{excluding} ones coerced to factors in the model formula)} \item{grid}{A \code{data.frame} (provided by \code{ref_grid}) containing the predictor settings needed in the reference grid} } \description{ Methods have been written that allow \code{glmmTMB} objects to be used with several downstream packages that enable different forms of inference. In particular, \itemize{ \item \code{car::Anova} constructs type-II and type-III Anova tables for the fixed effect parameters of the conditional model (this might work with the fixed effects of the zero-inflation or dispersion models, but has not been tested) \item the \code{effects} package computes graphical tabular effect displays (again, for the fixed effects of the conditional component) \item the \code{emmeans} package computes estimated marginal means (aka least-squares means) for the fixed effects of the conditional component } } \details{ While the examples below are disabled for earlier versions of R, they may still work; it may be necessary to refer to private versions of methods, e.g. \code{glmmTMB:::Anova.glmmTMB(model, ...)}. } glmmTMB/man/numFactor.Rd0000644000176200001440000000267013614324717014566 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils_covstruct.R \name{numFactor} \alias{numFactor} \alias{parseNumLevels} \title{Factor with numeric interpretable levels.} \usage{ numFactor(x, ...) parseNumLevels(levels) } \arguments{ \item{x}{Vector, matrix or data.frame that constitute the coordinates.} \item{...}{Additional vectors, matrices or data.frames that constitute the coordinates.} \item{levels}{Character vector to parse into numeric values.} } \value{ Factor with specialized coding of levels. } \description{ Create a factor with numeric interpretable factor levels. } \details{ Some \code{glmmTMB} covariance structures require extra information, such as temporal or spatial coordinates. \code{numFactor} allows to associate such extra information as part of a factor via the factor levels. The original numeric coordinates are recoverable without loss of precision using the function \code{parseNumLevels}. Factor levels are sorted coordinate wise from left to right: first coordinate is fastest running. } \examples{ ## 1D example numFactor(sample(1:5,20,TRUE)) ## 2D example coords <- cbind( sample(1:5,20,TRUE), sample(1:5,20,TRUE) ) (f <- numFactor(coords)) parseNumLevels(levels(f)) ## Sorted ## Used as part of a model.matrix model.matrix( ~f ) ## parseNumLevels( colnames(model.matrix( ~f )) ) ## Error: 'Failed to parse numeric levels: (Intercept)' parseNumLevels( colnames(model.matrix( ~ f-1 )) ) } glmmTMB/man/formatVC.Rd0000644000176200001440000000226213614324717014346 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/VarCorr.R \name{formatVC} \alias{formatVC} \title{Format the 'VarCorr' Matrix of Random Effects} \usage{ formatVC( varcor, digits = max(3, getOption("digits") - 2), comp = "Std.Dev.", formatter = format, useScale = attr(varcor, "useSc"), ... ) } \arguments{ \item{varcor}{a \code{\link{VarCorr}} (-like) matrix with attributes.} \item{digits}{the number of significant digits.} \item{comp}{character vector of length one or two indicating which columns out of "Variance" and "Std.Dev." should be shown in the formatted output.} \item{formatter}{the \code{\link{function}} to be used for formatting the standard deviations and or variances (but \emph{not} the correlations which (currently) are always formatted as "0.nnn"} \item{useScale}{whether to report a scale parameter (e.g. residual standard deviation)} \item{...}{optional arguments for \code{formatter(*)} in addition to the first (numeric vector) and \code{digits}.} } \value{ a character matrix of formatted VarCorr entries from \code{varc}. } \description{ "format()" the 'VarCorr' matrix of the random effects -- for print()ing and show()ing } glmmTMB/man/profile.glmmTMB.Rd0000644000176200001440000000642513614324717015570 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/profile.R \name{profile.glmmTMB} \alias{profile.glmmTMB} \alias{confint.profile.glmmTMB} \title{Compute likelihood profiles for a fitted model} \usage{ \method{profile}{glmmTMB}( fitted, parm = NULL, level_max = 0.99, npts = 8, stepfac = 1/4, stderr = NULL, trace = FALSE, parallel = c("no", "multicore", "snow"), ncpus = getOption("profile.ncpus", 1L), cl = NULL, ... ) \method{confint}{profile.glmmTMB}(object, parm = NULL, level = 0.95, ...) } \arguments{ \item{fitted}{a fitted \code{glmmTMB} object} \item{parm}{which parameters to profile, specified \itemize{ \item by index (position) \item by name (matching the row/column names of \code{vcov(object,full=TRUE)}) \item as \code{"theta_"} (random-effects variance-covariance parameters) or \code{"beta_"} (conditional and zero-inflation parameters) }} \item{level_max}{maximum confidence interval target for profile} \item{npts}{target number of points in (each half of) the profile (\emph{approximate})} \item{stepfac}{initial step factor (fraction of estimated standard deviation)} \item{stderr}{standard errors to use as a scaling factor when picking step sizes to compute the profile; by default (if \code{stderr} is \code{NULL}, or \code{NA} for a particular element), uses the estimated (Wald) standard errors of the parameters} \item{trace}{print tracing information? If \code{trace=FALSE} or 0, no tracing; if \code{trace=1}, print names of parameters currently being profiled; if \code{trace>1}, turn on tracing for the underlying \code{\link{tmbprofile}} function} \item{parallel}{method (if any) for parallel computation} \item{ncpus}{number of CPUs/cores to use for parallel computation} \item{cl}{cluster to use for parallel computation} \item{...}{additional arguments passed to \code{\link{tmbprofile}}} \item{object}{a fitted profile (\code{profile.glmmTMB}) object} \item{level}{confidence level} } \value{ An object of class \code{profile.glmmTMB}, which is also a data frame, with columns \code{.par} (parameter being profiled), \code{.focal} (value of focal parameter), value (negative log-likelihood). } \description{ Compute likelihood profiles for a fitted model } \details{ Fits natural splines separately to the points from each half of the profile for each specified parameter (i.e., values above and below the MLE), then finds the inverse functions to estimate the endpoints of the confidence interval } \examples{ \dontrun{ m1 <- glmmTMB(count~ mined + (1|site), zi=~mined, family=poisson, data=Salamanders) salamander_prof1 <- profile(m1, parallel="multicore", ncpus=2, trace=1) ## testing salamander_prof1 <- profile(m1, trace=1,parm=1) salamander_prof1M <- profile(m1, trace=1,parm=1, npts = 4) salamander_prof2 <- profile(m1, parm="theta_") } salamander_prof1 <- readRDS(system.file("example_files","salamander_prof1.rds",package="glmmTMB")) if (require("ggplot2")) { ggplot(salamander_prof1,aes(.focal,sqrt(value))) + geom_point() + geom_line()+ facet_wrap(~.par,scale="free_x")+ geom_hline(yintercept=1.96,linetype=2) } salamander_prof1 <- readRDS(system.file("example_files","salamander_prof1.rds",package="glmmTMB")) confint(salamander_prof1) confint(salamander_prof1,level=0.99) } glmmTMB/DESCRIPTION0000644000176200001440000000571113616106055013266 0ustar liggesusersPackage: glmmTMB Title: Generalized Linear Mixed Models using Template Model Builder Version: 1.0.0 Authors@R: c(person("Arni","Magnusson",role="aut", comment=c(ORCID="0000-0003-2769-6741")), person("Hans","Skaug",role="aut"), person("Anders","Nielsen",role="aut", comment=c(ORCID="0000-0001-9683-9262")), person("Casper","Berg",role="aut", comment=c(ORCID="0000-0002-3812-5269")), person("Kasper","Kristensen",role="aut"), person("Martin","Maechler",role="aut", comment=c(ORCID="0000-0002-8685-9910")), person("Koen","van Bentham",role="aut"), person("Ben","Bolker",role="aut", comment=c(ORCID="0000-0002-2127-0443")), person("Nafis","Sadat",role="ctb", comment=c(ORCID="0000-0001-5715-616X")), person("Daniel","Lüdecke", role="ctb", comment=c(ORCID="0000-0002-8895-3206")), person("Russ","Lenth", role="ctb"), person("Mollie","Brooks",role=c("aut","cre"), email="mollieebrooks@gmail.com", comment=c(ORCID="0000-0001-6963-8326"))) Description: Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation. License: AGPL-3 Depends: R (>= 3.2.0) Imports: methods, TMB (>= 1.7.14), lme4 (>= 1.1-18.9000), Matrix, nlme LinkingTo: TMB, RcppEigen Suggests: knitr, rmarkdown, testthat, MASS, lattice, ggplot2 (>= 2.2.1), mlmRev, bbmle (>= 1.0.19), pscl, coda, reshape2, car (>= 3.0.6), emmeans (>= 1.4), estimability, DHARMa, multcomp, MuMIn, effects (>= 4.0-1), dotwhisker, broom, broom.mixed, plyr, png, boot, texreg, xtable, huxtable SystemRequirements: GNU make VignetteBuilder: knitr URL: https://github.com/glmmTMB LazyData: TRUE BugReports: https://github.com/glmmTMB/glmmTMB/issues RoxygenNote: 7.0.2 NeedsCompilation: yes Encoding: UTF-8 Packaged: 2020-02-03 18:18:40 UTC; molliebrooks Author: Arni Magnusson [aut] (), Hans Skaug [aut], Anders Nielsen [aut] (), Casper Berg [aut] (), Kasper Kristensen [aut], Martin Maechler [aut] (), Koen van Bentham [aut], Ben Bolker [aut] (), Nafis Sadat [ctb] (), Daniel Lüdecke [ctb] (), Russ Lenth [ctb], Mollie Brooks [aut, cre] () Maintainer: Mollie Brooks Repository: CRAN Date/Publication: 2020-02-03 21:10:05 UTC glmmTMB/build/0000755000176200001440000000000013616062000012642 5ustar liggesusersglmmTMB/build/vignette.rds0000644000176200001440000000067313616062000015207 0ustar liggesusersRR0,DQf^|A.3Ҵd&i:I*$%)C~v&/=|=K=^WߤR`!; 1#=n$Ri0/$R -)+Ibԏ)cC0Oֆ1b ͠"<)jZoLB"Kʥ[qfؐ׹vL)L0$RZ>Oaxm(Pa* }f#`L؞a+ŨaE+ozPwNۏ4#* *Zۧ0\U-biWhil8\6|f2dcJ OsAT ϓiOp\h|PWu`i~U! Ud^ "kCaglmmTMB/tests/0000755000176200001440000000000013616062000012705 5ustar liggesusersglmmTMB/tests/testthat/0000755000176200001440000000000013616062000014545 5ustar liggesusersglmmTMB/tests/testthat/test-predict.R0000644000176200001440000002326413614324717017324 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB")) data(sleepstudy, package = "lme4") sleepstudy <- transform(sleepstudy, DaysFac = factor(cut(Days,2)) ) ssNA <- transform(sleepstudy, Days = replace(Days,c(1,27,93,145), NA)) ssNA2 <- transform(sleepstudy, Days = replace(Days,c(2,49), NA)) data(cbpp, package = "lme4") set.seed(101) cbpp_zi <- cbpp cbpp_zi[sample(nrow(cbpp),size=15,replace=FALSE),"incidence"] <- 0 ## 'newdata' nd <- subset(sleepstudy, Subject=="308", select=-1) nd$Subject <- "new" nd$DaysFac <- "new" context("Predicting new levels") g0 <- glmmTMB(Reaction ~ Days + (Days|Subject), sleepstudy) test_that("manual prediction of pop level pred", { prnd <- predict(g0, newdata=nd, allow.new.levels=TRUE) expect_equal( as.numeric(prnd), fixef(g0)$cond[1] + fixef(g0)$cond[2] * nd$Days , tol=1e-10) }) test_that("population-level prediction", { prnd <- predict(g0) expect_equal(length(unique(prnd)),180) prnd2 <- predict(g0, re.form=~0) prnd3 <- predict(g0, re.form=NA) expect_equal(prnd2,prnd3) expect_equal(length(unique(prnd2)),10) ## make sure we haven't messed up any internal structures ... prnd4 <- predict(g0) expect_equal(prnd, prnd4) }) context("Catch invalid predictions") test_that("new levels of fixed effect factor", { g1 <- glmmTMB(Reaction ~ Days + Subject, sleepstudy) expect_error( predict(g1, nd), "Prediction is not possible for unknown fixed effects") }) test_that("new levels in RE term", { g2 <- glmmTMB(Reaction ~ us(DaysFac | Subject), sleepstudy) expect_error( predict(g2, nd), "Prediction is not possible for terms") }) test_that("new levels in AR1 (OK)", { g3 <- glmmTMB(Reaction ~ ar1(DaysFac + 0| Subject), sleepstudy) expect_warning( predict(g3, nd), ## OK: AR1 does not introduce new parameters "Predicting new random effect levels") }) context("Predict two-column response case") test_that("two-column response", { fm <- glmmTMB( cbind(count,4) ~ mined, family=betabinomial, data=Salamanders) expect_equal(predict(fm, type="response"), c(0.05469247, 0.29269818)[Salamanders$mined] ) }) context("Prediction with dispformula=~0") y <- 1:10 f <- glmmTMB(y ~ 1, dispformula=~0) expect_equal(predict(f), rep(5.5, 10)) context("Handling NA values in predictions") ss <- sleepstudy g0_ex <- update(g0, data=ssNA, na.action=na.exclude) g0_om <- update(g0, data=ssNA, na.action=na.omit) pp_ex <- predict(g0_ex) pp_om <- predict(g0_om) expect_equal(length(pp_ex),nrow(ssNA)) expect_true(all(is.na(pp_ex)==is.na(ssNA$Days))) expect_equal(length(pp_om),length(na.omit(ssNA$Days))) expect_true(!any(is.na(pp_om))) ## na.pass pp_ndNA <- predict(g0,newdata=ssNA) expect(all(is.na(ssNA$Days)==is.na(pp_ndNA)), failure_message="NAs don't match with na.pass+predict") pp_ndNA2 <- predict(g0,newdata=ssNA2) expect(all(is.na(ssNA2$Days)==is.na(pp_ndNA2)), failure_message="NAs don't match with na.pass+predict+newdata") ## na.omit pp_ndNA_om <- predict(g0,newdata=ssNA,na.action=na.omit) expect_equal(length(pp_ndNA_om),sum(complete.cases(ssNA))) context("prediction with different binomial specs") tmbm1 <- glmmTMB(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) tmbm2 <- update(tmbm1,incidence/size ~ . , weights = size) test_that("fitted & predicted agree", { expect_equal(fitted(tmbm1),fitted(tmbm2)) expect_equal(predict(tmbm1),predict(tmbm2)) }) context("zero-inflation prediction") g0_zi <- update(tmbm2, ziformula = ~period) un <- function(x) lapply(x,unname) mypred <- function(form,dd,cc,vv,linkinv=identity,mu.eta=NULL) { X <- model.matrix(form,dd) pred <- drop(X %*% cc) se <- drop(sqrt(diag(X %*% vv %*% t(X)))) if (!is.null(mu.eta)) se <- se*mu.eta(pred) pred <- linkinv(pred) return(un(list(fit=pred,se.fit=se))) } ## FIXME: predictions should have row names of data dd <- data.frame(unique(cbpp["period"]),size=1,herd=NA) ff <- make.link("logit") test_that("type='link'", { link_pred <- mypred(~period,dd,fixef(g0_zi)$cond,vcov(g0_zi)$cond) expect_equal(un(predict(g0_zi,newdata=dd,se.fit=TRUE)), link_pred) }) test_that("various types", { cond_pred <- mypred(~period,dd,fixef(g0_zi)$cond,vcov(g0_zi)$cond, ff$linkinv,ff$mu.eta) expect_equal(un(predict(g0_zi,newdata=dd,se.fit=TRUE,type="conditional")), cond_pred) zprob_pred <- mypred(~period,dd,fixef(g0_zi)$zi,vcov(g0_zi)$zi, ff$linkinv,ff$mu.eta) expect_equal(un(predict(g0_zi,newdata=dd,se.fit=TRUE,type="zprob")), zprob_pred) expect_equal(unname(predict(g0_zi,newdata=dd,se.fit=TRUE,type="response")$fit), cond_pred$fit*(1-zprob_pred$fit)) }) test_that("type='zlink'", { zlink_pred <- mypred(~period,dd,fixef(g0_zi)$zi,vcov(g0_zi)$zi) expect_equal(un(predict(g0_zi,newdata=dd,se.fit=TRUE,type="zlink")), zlink_pred) }) context("deprecated zitype parameter") expect_warning(predict(g0_zi,newdata=dd,zitype="zprob")) context("complex bases") data("sleepstudy",package="lme4") nd <- data.frame(Days=0, Subject=factor("309", levels=levels(sleepstudy$Subject))) test_that("poly", { g1 <- glmmTMB(Reaction~poly(Days,3), sleepstudy) expect_equal(predict(g1, newdata=data.frame(Days=0)), 255.7690496, tolerance=1e-5) }) test_that("splines", { if (getRversion()>="3.5.1") { ## work around predict/predvars bug in 3.5.0 & previous versions g2 <- glmmTMB(Reaction~splines::ns(Days,5), sleepstudy) } else { library(splines) g2 <- glmmTMB(Reaction~ns(Days,5), sleepstudy) } expect_equal(predict(g2, newdata=data.frame(Days=0)),257.42672, tolerance=1e-5) }) test_that("scale", { g3 <- glmmTMB(Reaction~scale(Days), sleepstudy) expect_equal(predict(g3, newdata=data.frame(Days=0)), 251.40507651, tolerance=1e-5) }) test_that("poly_RE", { g1 <- glmmTMB(Reaction~(1|Subject) + poly(Days,3), sleepstudy) expect_equal(predict(g1, newdata=nd, allow.new.levels=TRUE), 178.1629812, tolerance=1e-5) }) test_that("splines_RE", { if (getRversion()>="3.5.1") { g2 <- glmmTMB(Reaction~(1|Subject) + splines::ns(Days,5), sleepstudy) } else { library(splines) g2 <- glmmTMB(Reaction~(1|Subject) + ns(Days,5), sleepstudy) } expect_equal(predict(g2, newdata=nd, allow.new.levels=TRUE), 179.7784754, tolerance=1e-5) }) test_that("scale_RE", { g3 <- glmmTMB(Reaction~(1|Subject) + scale(Days), sleepstudy) expect_equal(predict(g3, newdata=nd, allow.new.levels=TRUE), 173.83923026, tolerance=1e-5) }) test_that("complex bases in dispformula", { g4A <- glmmTMB(Reaction~1, sleepstudy) g4B <- glmmTMB(Reaction~1, disp=~poly(Days,2), sleepstudy) expect_equal(predict(g4A, newdata=nd, se.fit=TRUE), list(fit = 298.507945749154, se.fit = 4.18682101029576), tolerance=1e-5) expect_equal(predict(g4B, newdata=nd, se.fit=TRUE), list(fit = 283.656705454758, se.fit = 4.74204256781178)) }) test_that("fix_predvars works for I(x^2)", { ## GH512; @strengejacke set.seed(123) n <- 500 d <- data.frame( y = rbinom(n, size = 1, prob = .2), x = rnorm(n), site = sample(letters, size = n, replace = TRUE), area = sample(LETTERS[1:9], size = n, replace = TRUE) ) form <- y ~ x + I(x^2) + I(x^3) + (1 | area) m1 <- lme4::glmer(form, family = binomial("logit"), data = d) m2 <- glmmTMB(form, family = binomial("logit"), data = d) nd <- data.frame(x = c(-2, -1, 0, 1, 2), area = NA) p1 <- predict(m1, newdata = nd, type = "link", re.form = NA) p2 <- predict(m2, newdata = nd, type = "link") expect_equal(unname(p1),unname(p2), tolerance=1e-4) }) test_that("contrasts carried over", { ## GH 439, @cvoeten iris2 <- transform(iris, grp=c("a","b")) contrasts(iris2$Species) <- contr.sum contrasts(iris2$grp) <- contr.sum mod1 <- glmmTMB(Sepal.Length ~ Species,iris) mod2 <- glmmTMB(Sepal.Length ~ Species,iris2) iris3 <- iris[1,] iris3$Species <- "extra" ## these are not *exactly* equal because of numeric differences ## when estimating parameters differently ... (?) expect_equal(predict(mod1),predict(mod2),tolerance=1e-6) ## make sure we actually imposed contrasts correctly/differently expect_false(isTRUE(all.equal(fixef(mod1)$cond,fixef(mod2)$cond))) expect_error(predict(mod1,newdata=iris2), "contrasts mismatch") expect_equal(predict(mod1,newdata=iris2,allow.new.levels=TRUE), predict(mod1,newdata=iris)) mod3 <- glmmTMB(Sepal.Length ~ 1|Species, iris) expect_equal(c(predict(mod3,newdata=data.frame(Species="ABC"), allow.new.levels=TRUE)), 5.843333, tolerance=1e-6) mod4 <- glmmTMB(Sepal.Length ~ grp + (1|Species), iris2) expect_equal(c(predict(mod4, newdata=data.frame(Species="ABC",grp="a"), allow.new.levels=TRUE)), 5.839998, tolerance=1e-6) ## works with char rather than factor in new group vble expect_equal(predict(mod3, newdata=iris3, allow.new.levels=TRUE), 5.843333, tolerance=1e-6) }) test_that("dispersion", { mod5 <- glmmTMB(Sepal.Length ~ Species, disp=~ Species, iris) expect_equal(length(unique(predict(mod5, type="disp"))), length(unique(iris$Species))) expect_equal(length(unique(predict(mod5, type="disp", se.fit=TRUE)$se.fit)), length(unique(iris$Species))) })glmmTMB/tests/testthat/test-reml.R0000644000176200001440000000276513614324717016634 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB"), require("lme4")) context("REML") test_that("REML check against lmer", { ## Example 1: Compare results with lmer fm1.lmer <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy, REML=TRUE) fm1.glmmTMB <- glmmTMB(Reaction ~ Days + (Days | Subject), sleepstudy, REML=TRUE) expect_equal( logLik(fm1.lmer) , logLik(fm1.glmmTMB) ) expect_equal(as.vector(predict(fm1.lmer)) , predict(fm1.glmmTMB), tolerance=2e-3) expect_equal(vcov(fm1.glmmTMB)$cond, as.matrix(vcov(fm1.lmer)) , tolerance=1e-3) ## Example 2: Compare results with lmer data(Orthodont,package="nlme") Orthodont$nsex <- as.numeric(Orthodont$Sex=="Male") Orthodont$nsexage <- with(Orthodont, nsex*age) fm2.lmer <- lmer(distance ~ age + (age|Subject) + (0+nsex|Subject) + (0 + nsexage|Subject), data=Orthodont, REML=TRUE, control=lmerControl(check.conv.grad = .makeCC("warning", tol = 5e-3))) fm2.glmmTMB <- glmmTMB(distance ~ age + (age|Subject) + (0+nsex|Subject) + (0 + nsexage|Subject), data=Orthodont, REML=TRUE) expect_equal( logLik(fm2.lmer) , logLik(fm2.glmmTMB), tolerance=1e-5 ) expect_equal(as.vector(predict(fm2.lmer)) , predict(fm2.glmmTMB), tolerance=1e-4) expect_equal(vcov(fm2.glmmTMB)$cond, as.matrix(vcov(fm2.lmer)) , tolerance=1e-3) }) glmmTMB/tests/testthat/test-varstruc.R0000644000176200001440000001145513614324717017542 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB"), require("lme4")) source(system.file("test_data/glmmTMB-test-funs.R", package="glmmTMB", mustWork=TRUE)) data(sleepstudy, cbpp, package = "lme4") sleepstudy <- transform(sleepstudy,fDays=cut(Days,c(0,3,6,10),right=FALSE), row=factor(seq(nrow(sleepstudy)))) context("variance structures") ## two equivalent diagonal constructions fm_diag1 <- glmmTMB(Reaction ~ Days + diag(Days| Subject), sleepstudy) fm_diag2 <- glmmTMB(Reaction ~ Days + ( 1 | Subject) + (0+Days | Subject), sleepstudy) fm_diag2_lmer <- lmer(Reaction ~ Days + ( 1 | Subject) + (0+Days | Subject), sleepstudy, REML=FALSE) fm_us1 <- glmmTMB(Reaction ~ Days + (Days| Subject), sleepstudy) fm_cs1 <- glmmTMB(Reaction ~ Days + cs(Days| Subject), sleepstudy) fm_us1_lmer <- lmer(Reaction ~ Days + ( Days | Subject), sleepstudy, REML=FALSE) fm_cs2 <- glmmTMB(Reaction ~ Days + cs(fDays| Subject), sleepstudy) ## these would be equivalent to a compound symmetry model with *homog* variance fm_nest <- glmmTMB(Reaction ~ Days + (1| Subject/fDays), sleepstudy) fm_nest_lmer <- lmer(Reaction ~ Days + (1|Subject/fDays), sleepstudy, REML=FALSE) ## model with ~ Days + ... gives non-pos-def Hessian fm_ar1 <- glmmTMB(Reaction ~ 1 + (1|Subject) + ar1(row+0| Subject), sleepstudy) test_that("diag", { ## two formulations of diag and lme4 all give same log-lik expect_equal(logLik(fm_diag1),logLik(fm_diag2_lmer)) expect_equal(logLik(fm_diag1),logLik(fm_diag2)) }) test_that("cs_us", { ## for a two-level factor, compound symmetry and unstructured ## give same result expect_equal(logLik(fm_us1),logLik(fm_cs1)) expect_equal(logLik(fm_us1),logLik(fm_us1_lmer)) }) test_that("cs_homog", { ## *homogenous* compound symmetry vs. nested random effects expect_equal(logLik(fm_nest),logLik(fm_nest_lmer)) }) test_that("basic ar1", { vv <- VarCorr(fm_ar1)[["cond"]] cc <- cov2cor(vv[[2]]) expect_equal(cc[1,],cc[,1]) expect_equal(unname(cc[1,]), cc[1,2]^(0:(nrow(cc)-1))) }) test_that("print ar1 (>1 RE)", { cco <- gsub(" +"," ", trimws(capture.output(print(summary(fm_ar1),digits=1)))) expect_equal(cco[12:14], c("Subject (Intercept) 4e-01 0.6", "Subject.1 row1 4e+03 60.8 0.87 (ar1)", "Residual 8e+01 8.9")) }) ## FIXME: simpler to check formatVC() directly? get_vcout <- function(x,g="\\bSubject\\b") { cc <- capture.output(print(VarCorr(x))) cc1 <- grep(g,cc,value=TRUE,perl=TRUE) ss <- strsplit(cc1,"[^[:alnum:][:punct:]]+")[[1]] return(ss[nchar(ss)>0]) } test_that("varcorr_print", { ss <- get_vcout(fm_cs1) expect_equal(length(ss),5) expect_equal(ss[4:5],c("0.081","(cs)")) ss2 <- get_vcout(fm_ar1,g="\\bSubject.1\\b") expect_equal(length(ss2),5) expect_equal(ss2[4:5],c("0.873","(ar1)")) ## test case with two different size V-C set.seed(101) dd <- data.frame(y=rnorm(1000),c=factor(rep(1:2,500)), w=factor(rep(1:10,each=100)), s=factor(rep(1:10,100))) ## non-pos-def case (we don't care at the moment) m1 <- suppressWarnings(glmmTMB(y~c+(c|w)+(1|s),data=dd, family=gaussian)) cc <- squash_white(capture.output(print(VarCorr(m1),digits=2))) expect_equal(cc, c("Conditional model:", "Groups Name Std.Dev. Corr", "w (Intercept) 3.1e-05", "c2 4.9e-06 0.98", "s (Intercept) 3.4e-05", "Residual 9.6e-01")) }) test_that("cov_struct_order", { ff <- system.file("test_data","cov_struct_order.rds",package="glmmTMB") if (nchar(ff)>0) { dat <- readRDS(ff) } else { set.seed(101) nb <- 100 ns <- nb*3 nt <- 100 cor <- .7 dat <- data.frame(Block = factor(rep(1:nb, each = ns/nb*nt)), Stand = factor(rep(1:ns, each = nt)), Time = rep(1:nt, times = ns), blockeff = rep(rnorm(nb, 0, .5), each = ns/nb*nt), standeff = rep(rnorm(ns, 0, .8), each = nt), resid = c(t(MASS::mvrnorm(ns, mu = rep(0, nt), Sigma = 1.2*cor^abs(outer(0:(nt-1),0:(nt-1),"-")))))) dat$y <- with(dat, 5 + blockeff + standeff + resid)+rnorm(nrow(dat), 0, .1) dat$Time <- factor(dat$Time) ## saveRDS(dat, file="../../inst/test_data/cov_struct_order.rds",version=2) } fit1 <- glmmTMB(y ~ (1|Block) + (1|Stand)+ ar1(Time +0|Stand), data = dat) expect_equal(unname(fit1$fit$par), c(4.98852432, -4.22220615, -0.76452645, -0.24762133, 0.08879302, 1.00022657), tol=1e-3) }) glmmTMB/tests/testthat/test-mapopt.R0000644000176200001440000000501713614324717017166 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB")) data(Salamanders, package = "glmmTMB") context("mapping") m1 <- glmmTMB(count~ mined, family=poisson, data=Salamanders, start=list(beta=c(0,2)), map=list(beta=factor(c(1,NA)))) m2 <- glmmTMB(count~ mined + (1|site), family=poisson, data=Salamanders, start=list(theta=log(2)), map=list(theta=factor(NA))) m3 <- glmmTMB(count~ mined + (1|site), zi = ~1, family=poisson, data=Salamanders, start=list(theta=log(2), betazi=c(-1)), map=list(theta=factor(NA), betazi=factor(NA))) m4_nomap <- glmmTMB(count~ mined + (1|site), zi=~mined, family=poisson, data=Salamanders) m4 <- glmmTMB(count~ mined + (1|site), zi=~mined, family=poisson, data=Salamanders, map=list(theta=factor(NA)), start = list(theta=log(10))) m1optim <- update(m1, control=glmmTMBControl(optimizer=optim, optArgs=list(method="BFGS"))) test_that("basic mapping works", { expect_equal(fixef(m1)$cond[[2]], 2.0) expect_equal(exp(getME(m2,"theta")), 2.0) expect_equal(fixef(m3)$zi[[1]], -1.0) }) test_that("predict works with mapped params", expect_equal(vapply(predict(m1,se.fit=TRUE),unique,numeric(1)), c(fit = -1.18646939995962, se.fit = 0.0342594326326737), tolerance=1e-6) ) test_that("vcov works with mapped params", { expect_equal(dim(vcov(m1)$cond),c(1,1)) expect_equal(dim(vcov(m1,full=TRUE)),c(1,1)) expect_equal(dim(vcov(m2)$cond),c(2,2)) expect_equal(dim(vcov(m2,full=TRUE)),c(2,2)) }) test_that("confint works with mapped params", { cm1 <- confint(m1) expect_equal(dim(cm1), c(1,3)) expect_equal(rownames(cm1), "(Intercept)") cm2 <- confint(m2) expect_equal(dim(cm2), c(2,3)) expect_equal(rownames(cm2), c("(Intercept)","minedno")) cm3 <- confint(m3) expect_equal(dim(cm3), c(2,3)) expect_equal(rownames(cm3), c("(Intercept)","minedno")) cm4 <- confint(m4) expect_equal(dim(cm4), c(4,3)) expect_equal(rownames(cm4), c("cond.(Intercept)", "cond.minedno", "zi.(Intercept)", "zi.minedno")) cm4_nomap <- confint(m4_nomap) }) context("alternate optimizers") test_that("alternate optimizers work", { expect_equal(fixef(m1),fixef(m1optim), tol=1e-4) expect_false(identical(fixef(m1),fixef(m1optim))) }) glmmTMB/tests/testthat/test-env.R0000644000176200001440000000253213614324717016455 0ustar liggesusers## check that everything works in weird environments stopifnot(require("testthat"), require("glmmTMB")) data(sleepstudy, cbpp, package = "lme4") ## need global env for test_that sleepstudy <<- transform(sleepstudy, DaysFac = factor(Days)) context("basic examples") test_that("basic example #1", { fitFun <- function(dat){ glmmTMB(Reaction ~ Days + (1|Subject), data=dat) } f0 <- glmmTMB(Reaction ~ Days + (1|Subject), data=sleepstudy) f1 <- fitFun(sleepstudy) uncall <- function(x) { x$call <- NULL return(x) } expect_equal(uncall(f0),uncall(f1)) }) test_that("paranoia", { formFun <- function() { return(Reaction ~ Days + (1|Subject)) } fitFun <- function(f,dat){ glmmTMB(f, data=dat) } f0 <- glmmTMB(Reaction ~ Days + (1|Subject), data=sleepstudy) f1 <- fitFun(formFun(),sleepstudy) uncall <- function(x) { x$call <- NULL return(x) } expect_equal(uncall(f0),uncall(f1)) }) test_that("dispformula env", { fitFun2 <- function(dat){ glmmTMB(count ~ 1, data=dat, family="poisson" ) } m0 <- fitFun2(Salamanders) m1 <- glmmTMB(count ~ 1, data= Salamanders, family="poisson") uncall <- function(x) { x$call <- NULL return(x) } expect_equal(uncall(summary(m0)), uncall(summary(m1))) }) glmmTMB/tests/testthat/test-VarCorr.R0000644000176200001440000001521513614324717017245 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB"), require("lme4")) source(system.file("test_data/glmmTMB-test-funs.R", package="glmmTMB", mustWork=TRUE)) context("VarCorr") ## --------------- data("Orthodont", package="nlme") fm1 <- glmmTMB(distance ~ age + (age|Subject), data = Orthodont) fm1C <- lmer(distance ~ age + (age|Subject), data = Orthodont, REML=FALSE, control=lmerControl(check.conv.grad = .makeCC("warning", tol = 2e-2))) gm1 <- glmmTMB(incidence/size ~ period + (1 | herd), weights=size, data = cbpp, family = binomial) gm1C <- glmer(incidence/size ~ period + (1 | herd), weights=size, data = cbpp, family = binomial) ## make glmmTMB VarCorr look like lme4 VarCorr stripTMBVC <- function(x) { r <- VarCorr(x)[["cond"]] for (i in seq_along(r)) { attr(r[[i]],"blockCode") <- NULL } return(r) } expect_equal(stripTMBVC(fm1),unclass(VarCorr(fm1C)), tol=2e-3) expect_equal(stripTMBVC(gm1),unclass(VarCorr(gm1C)), tol=5e-3) ## have to take only last 4 lines ## some white space diffs introduced in fancy-corr-printing pfun <- function(x) squash_white(capture.output(print(VarCorr(x),digits=2))) expect_equal(tail(pfun(fm1),4), pfun(fm1C)) data("Pixel", package="nlme") ## nPix <- nrow(Pixel) complex_form <- pixel ~ day + I(day^2) + (day | Dog) + (1 | Side/Dog) expect_warning(fmPix1 <<- glmmTMB(complex_form, data = Pixel), "convergence problem") fmPix1B <- lmer(complex_form, data = Pixel, control=lmerControl(check.conv.grad = .makeCC("warning", tol = 5e-3))) vPix1B <- unlist(lapply(VarCorr(fmPix1B),c)) vPix1 <- unlist(lapply(VarCorr(fmPix1)[["cond"]],c)) ## "manual" (1 | Dog / Side) : fmPix3 <- glmmTMB(pixel ~ day + I(day^2) + (day | Dog) + (1 | Dog) + (1 | Side:Dog), data = Pixel) vPix3 <- unlist(lapply(VarCorr(fmPix3)[["cond"]],c)) fmP1.r <- fmPix1$obj$env$report() ## str(fmP1.r) ## List of 4 ## $ corrzi: list() ## $ sdzi : list() ## $ corr :List of 3 ## ..$ : num [1, 1] 1 ## ..$ : num [1, 1] 1 ## ..$ : num [1:2, 1:2] 1 -0.598 -0.598 1 ## $ sd :List of 3 ## ..$ : num 16.8 ## ..$ : num 9.44 ## ..$ : num [1:2] 24.83 1.73 ## fmP1.r $ corr vv <- VarCorr(fmPix1) set.seed(12345) dd <- data.frame(a=gl(10,100), b = rnorm(1000)) test2 <- suppressMessages(simulate(~1+(b|a), newdata=dd, family=poisson, newparams= list(beta = c("(Intercept)" = 1), theta = c(1,1,1)))) ## Zero-inflation : set all i.0 indices to 0: i.0 <- sample(c(FALSE,TRUE), 1000, prob=c(.3,.7), replace=TRUE) test2[i.0, 1] <- 0 mydata <<- cbind(dd, test2) ## GLOBAL ## The zeros in the 10 groups: xx <- xtabs(~ a + (sim_1 == 0), mydata) ## FIXME: actually need to fit this! ## non-trivial dispersion model data(sleepstudy, package="lme4") fm3 <- glmmTMB(Reaction ~ Days + (1|Subject), dispformula=~ Days, sleepstudy) cc0 <- capture.output(print(fm3)) cc1 <- capture.output(print(summary(fm3))) expect_true(any(grepl("Dispersion model:",cc0))) expect_true(any(grepl("Dispersion model:",cc1))) ## FIXME: slow ( ~ 49 seconds ) ## ??? wrong context? # not simulated this way, but returns right structure test_that("weird variance structure", { mydata <- cbind(dd, test2) gm <- suppressWarnings(glmmTMB(sim_1 ~ 1+(b|a), zi = ~1+(b|a), data=mydata, family=poisson())) cc2 <- capture.output(print(gm)) expect_equal(sum(grepl("Zero-inflation model:",cc2)),3) }) ## eight updateCholesky() warnings .. which will suppress *unless* they are in the last iter. if (FALSE) { str(gm.r <- gm$obj$env$report()) ## List of 4 ## $ corrzi:List of 1 ## ..$ : num [1:2, 1:2] 1 0.929 0.929 1 ## $ sdzi :List of 1 ## ..$ : num [1:2] 3.03e-05 1.87e-04 ## $ corr :List of 1 ## ..$ : num [1:2, 1:2] 1 0.921 0.921 1 ## $ sd :List of 1 ## ..$ : num [1:2] 0.779 1.575 } vc <- VarCorr(fm1) ## default print method: standard dev and corr getVCText <- function(obj,...) { c1 <- capture.output(print(obj,...)) c1f <- read.fwf(textConnection(c1[-(1:3)]),header=FALSE, fill=TRUE, widths=c(10,12,9,6)) return(c1f[,-(1:2)]) ## just value columns } ##expect_equal(c1,c("", "Conditional model:", ## " Groups Name Std.Dev. Corr ", ## " Subject (Intercept) 2.19409 ", ## " age 0.21492 -0.581", ## " Residual 1.31004 ")) expect_equal(getVCText(vc), structure(list(V3 = c(2.1941, 0.21492, 1.31004), V4 = c(NA, -0.581, NA)), .Names = c("V3", "V4"), class = "data.frame", row.names = c(NA, -3L)), tolerance=1e-5) ## both variance and std.dev. c2 <- getVCText(vc,comp=c("Variance","Std.Dev."),digits=2) ## c2 <- capture.output(print(vc,comp=c("Variance","Std.Dev."),digits=2)) ## expect_equal(c2, ## c("", "Conditional model:", ## " Groups Name Variance Std.Dev. Corr ", ## " Subject (Intercept) 4.814 2.19 ", ## " age 0.046 0.21 -0.58", ## " Residual 1.716 1.31 ")) expect_equal(c2, structure(list(V3 = c(4.814, 0.046, 1.716), V4 = c(2.19, 0.21, 1.31)), .Names = c("V3", "V4"), class = "data.frame", row.names = c(NA, -3L))) ## variance only c3 <- getVCText(vc,comp=c("Variance")) ## c3 <- capture.output(print(vc,)) ## expect_equal(c3, ## c("", "Conditional model:", ## " Groups Name Variance Corr ", ## " Subject (Intercept) 4.814050 ", ## " age 0.046192 -0.581", ## " Residual 1.716203 ")) expect_equal(c3,structure(list(V3 = c(4.814071, 0.046192, 1.716208), V4 = c(NA, -0.581, NA)), .Names = c("V3", "V4"), class = "data.frame", row.names = c(NA, -3L)), tolerance=5e-5) if (FALSE) { ## not yet ... as.data.frame(vc) as.data.frame(vc,order="lower.tri") } Orthodont$units <- factor(seq(nrow(Orthodont))) fm0 <- glmmTMB(distance ~ age + (1|Subject) + (1|units), dispformula=~0, data = Orthodont) test_that("VarCorr omits resid when dispformula=~0", { expect_false(attr(VarCorr(fm0)$cond,"useSc")) ## Residual vars not printed expect_false(any(grepl("Residual",capture.output(print(VarCorr(fm0)))))) }) test_that("vcov(.,full=TRUE) works for zero-disp models", { expect_equal(dim(vcov(fm0,full=TRUE)),c(4,4)) }) glmmTMB/tests/testthat/test-edgecases.R0000644000176200001440000000077013614324717017612 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB")) context("test edge cases") test_that("profiling failure", { ## data from https://github.com/glmmTMB/glmmTMB/issues/399 dd <- readRDS(system.file("test_data","IC_comp_data.rds", package="glmmTMB")) expect_warning(glmmTMB( ProbDiv ~ stdQlty + stdLaying + (1|Year) + (1|Site) + (1|PairID), family = "binomial", control=glmmTMBControl(profile = TRUE), data = dd), "a Newton step failed") }) glmmTMB/tests/testthat/test-disp.R0000644000176200001440000000264313614324717016627 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB")) context("Testing dispersion") sim1=function(nfac=24, nt=100, facsd=.1, tsd=.15, mu=0, residsd=1) { dat=expand.grid(fac=factor(letters[1:nfac]), t= 1:nt) n=nrow(dat) dat$REfac=rnorm(nfac, sd= facsd)[dat$fac] dat$REt=rnorm(nt, sd= tsd)[dat$t] dat$x=rnorm(n, mean=mu, sd=residsd) + dat$REfac + dat$REt return(dat) } set.seed(101) d1=sim1(mu=100, residsd =10) d2=sim1(mu=200, residsd =5) d1=transform(d1, fac=paste0(fac, 1), disp="ten") d2=transform(d2, fac=paste0(fac, 2), disp="five") ## global assignment for testthat dat <<- rbind(d1, d2) m0 <<- glmmTMB(x~disp+(1|fac), dispformula=~disp, dat) test_that("disp calc", { expect_equal(unname(fixef(m0)$disp), c(log(10^2), log(5^2)-log(10^2)), tol=1e-2) }) test_that("predict dispersion", { expect_equal(predict(m0, type="disp"), c(rep(10, 24*100), rep(5, 24*100)), tol=1e-2) }) dat2 <<- rbind(head(d1, 50), head(d2, 50)) #smaller for faster fitting when not checking estimates ## suppress "... false convergence (8) ..." suppressWarnings(nbm0 <<- glmmTMB(round(x)~disp+(1|fac), ziformula=~0, dispformula=~disp, dat2, family=nbinom1, se=FALSE) ) pm0 <<- update(nbm0, family=poisson) ## suppress "... false convergence (8) ..." nbm1 <<- suppressWarnings(update(pm0, family=nbinom1)) test_that("update maintains dispformula in call", { expect_equal(getCall(nbm0), getCall(nbm1)) }) glmmTMB/tests/testthat/test-basics.R0000644000176200001440000002513013614324717017130 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB")) data(sleepstudy, cbpp, package = "lme4") data(quine, package="MASS") ## n.b. for test_that, this must be assigned within the global ## environment ... cbpp <<- transform(cbpp, prop = incidence/size, obs=factor(seq(nrow(cbpp)))) ## utility: hack/replace parts of the updated result that will ## be cosmetically different matchForm <- function(obj, objU, family=FALSE) { for(cmp in c("call","frame")) # <- more? objU[[cmp]] <- obj[[cmp]] ## Q: why are formulas equivalent but not identical? A: their environments may differ objU$modelInfo$allForm <- obj$modelInfo$allForm if (family) objU$modelInfo$family <- obj$modelInfo$family return(objU) } context("Very basic glmmTMB fitting") lm0 <- lm(Reaction~Days,sleepstudy) fm00 <- glmmTMB(Reaction ~ Days, sleepstudy) fm0 <- glmmTMB(Reaction ~ 1 + ( 1 | Subject), sleepstudy) fm1 <- glmmTMB(Reaction ~ Days + ( 1 | Subject), sleepstudy) fm2 <- glmmTMB(Reaction ~ Days + (Days| Subject), sleepstudy) fm3 <- glmmTMB(Reaction ~ Days + ( 1 | Subject) + (0+Days | Subject), sleepstudy) test_that("Basic Gaussian Sleepdata examples", { expect_is(fm00, "glmmTMB") expect_is(fm0, "glmmTMB") expect_is(fm1, "glmmTMB") expect_is(fm2, "glmmTMB") expect_is(fm3, "glmmTMB") expect_equal(fixef(fm00)[[1]],coef(lm0),tol=1e-5) expect_equal(sigma(fm00)*sqrt(nobs(fm00)/(df.residual(fm00)+1)), summary(lm0)$sigma,tol=1e-5) expect_equal(fixef(fm0)[[1]], c("(Intercept)" = 298.508), tolerance = .0001) expect_equal(fixef(fm1)[[1]], c("(Intercept)" = 251.405, Days = 10.4673), tolerance = .0001) expect_equal(fixef(fm2)$cond, fixef(fm1)$cond, tolerance = 1e-5)# seen 1.042 e-6 expect_equal(fixef(fm3)$cond, fixef(fm1)$cond, tolerance = 5e-6)# seen 2.250 e-7 expect_equal(head(ranef(fm0)$cond$Subject[,1],3), c(37.4881849228705, -71.5589277273216, -58.009085500647), tolerance=1e-5) ## test *existence* of summary method -- nothing else for now expect_is(suppressWarnings(summary(fm3)),"summary.glmmTMB") }) test_that("Update Gaussian", { ## call doesn't match (formula gets mangled?) ## timing different fm1u <- update(fm0, . ~ . + Days) expect_equal(fm1, matchForm(fm1, fm1u)) }) test_that("Variance structures", { ## above: fm2 <- glmmTMB(Reaction ~ Days + (Days| Subject), sleepstudy) expect_is(fm2us <- glmmTMB(Reaction ~ Days + us(Days| Subject), sleepstudy), "glmmTMB") expect_is(fm2cs <- glmmTMB(Reaction ~ Days + cs(Days| Subject), sleepstudy), "glmmTMB") expect_is(fm2diag <- glmmTMB(Reaction ~ Days + diag(Days| Subject), sleepstudy), "glmmTMB") expect_equal(getME(fm2, "theta"), getME(fm2us,"theta")) ## FIXME: more here, compare results against lme4 ... }) test_that("Sleepdata Variance components", { expect_equal(c(unlist(VarCorr(fm3))), c(cond.Subject = 584.247907378213, cond.Subject.1 = 33.6332741779585), tolerance=1e-5) }) gm0 <<- glmmTMB(cbind(incidence, size-incidence) ~ 1 + (1|herd), data = cbpp, family=binomial()) gm1 <<- glmmTMB(cbind(incidence, size-incidence) ~ period + (1|herd), data = cbpp, family=binomial()) test_that("Basic Binomial CBPP examples", { ## Basic Binomial CBPP examples ---- intercept-only fixed effect expect_is(gm0, "glmmTMB") expect_is(gm1, "glmmTMB") expect_equal(fixef(gm0)[[1]], c("(Intercept)" = -2.045671), tolerance = 1e-3)#lme4 results expect_equal(fixef(gm1)[[1]], c("(Intercept)" = -1.398343,#lme4 results period2 = -0.991925, period3 = -1.128216, period4 = -1.579745), tolerance = 1e-3) # <- TODO: lower eventually }) test_that("Multiple RE, reordering", { ### Multiple RE, reordering tmb1 <- glmmTMB(cbind(incidence, size-incidence) ~ period + (1|herd) + (1|obs), data = cbpp, family=binomial()) tmb2 <- glmmTMB(cbind(incidence, size-incidence) ~ period + (1|obs) + (1|herd), data = cbpp, family=binomial()) expect_equal(fixef(tmb1), fixef(tmb2), tolerance = 1e-8) expect_equal(getME(tmb1, "theta"), getME(tmb2, "theta")[c(2,1)], tolerance = 5e-7) }) test_that("Alternative family specifications [via update(.)]", { ## intercept-only fixed effect res_chr <- matchForm(gm0, update(gm0, family= "binomial")) expect_equal(gm0, res_chr) expect_equal(gm0, matchForm(gm0, update(gm0, family= binomial()))) expect_warning(res_list <- matchForm(gm0, update(gm0, family= list(family = "binomial", link = "logit")), family=TRUE)) expect_equal(gm0, res_list) }) test_that("Update Binomial", { ## matchForm(): call doesn't match (formula gets mangled?) ## timing different gm1u <- update(gm0, . ~ . + period) expect_equal(gm1, matchForm(gm1, gm1u)) }) test_that("internal structures", { ## RE terms only in cond and zi model, not disp: GH #79 expect_equal(names(fm0$modelInfo$reTrms), c("cond","zi")) }) test_that("close to lme4 results", { expect_true(require("lme4")) L <- load(system.file("testdata", "lme-tst-fits.rda", package="lme4", mustWork=TRUE)) expect_is(L, "character") message("Loaded testdata from lme4:\n ", paste(strwrap(paste(L, collapse = ", ")), collapse = "\n ")) if(FALSE) { ## part of the above [not recreated here for speed mostly:] ## intercept only in both fixed and random effects fit_sleepstudy_0 <- lmer(Reaction ~ 1 + ( 1 | Subject), sleepstudy) ## fixed slope, intercept-only RE fit_sleepstudy_1 <- lmer(Reaction ~ Days + ( 1 | Subject), sleepstudy) ## fixed slope, intercept & slope RE fit_sleepstudy_2 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ## fixed slope, independent intercept & slope RE fit_sleepstudy_3 <- lmer(Reaction ~ Days + (1|Subject)+ (0+Days|Subject), sleepstudy) cbpp$obs <- factor(seq(nrow(cbpp))) ## intercept-only fixed effect fit_cbpp_0 <- glmer(cbind(incidence, size-incidence) ~ 1 + (1|herd), cbpp, family=binomial) ## include fixed effect of period fit_cbpp_1 <- update(fit_cbpp_0, . ~ . + period) ## include observation-level RE fit_cbpp_2 <- update(fit_cbpp_1, . ~ . + (1|obs)) ## specify formula by proportion/weights instead fit_cbpp_3 <- update(fit_cbpp_1, incidence/size ~ period + (1 | herd), weights = size) } ## What we really want to compare against - Maximum Likelihood (package 'DESCRIPTION' !) fi_0 <- lmer(Reaction ~ 1 + ( 1 | Subject), sleepstudy, REML=FALSE) fi_1 <- lmer(Reaction ~ Days + ( 1 | Subject), sleepstudy, REML=FALSE) fi_2 <- lmer(Reaction ~ Days + (Days| Subject), sleepstudy, REML=FALSE) fi_3 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy, REML=FALSE) ## Now check closeness to lme4 results ## ...................................... }) context("trickier examples") data(Owls) ## is <<- necessary ... ? Owls <- transform(Owls, ArrivalTime=scale(ArrivalTime,center=TRUE,scale=FALSE), NCalls= SiblingNegotiation) test_that("basic zero inflation", { expect_true(require("pscl")) o0.tmb <- glmmTMB(NCalls~(FoodTreatment + ArrivalTime) * SexParent + offset(logBroodSize), ziformula=~1, data = Owls, family=poisson(link = "log")) o0.pscl <-zeroinfl(NCalls~(FoodTreatment + ArrivalTime) * SexParent + offset(logBroodSize)|1, data = Owls) expect_equal(summary(o0.pscl)$coefficients$count, summary(o0.tmb)$coefficients$cond, tolerance=1e-5) expect_equal(summary(o0.pscl)$coefficients$zero, summary(o0.tmb)$coefficients$zi, tolerance=1e-5) o1.tmb <- glmmTMB(NCalls~(FoodTreatment + ArrivalTime) * SexParent + offset(logBroodSize) + diag(1 | Nest), ziformula=~1, data = Owls, family=poisson(link = "log")) expect_equal(ranef(o1.tmb)$cond$Nest[1,1], -0.484, tolerance=1e-2) #glmmADMB gave -0.4842771 }) test_that("alternative binomial model specifications", { d <<- data.frame(y=1:10,N=20,x=1) ## n.b. global assignment for testthat m0 <- suppressWarnings(glmmTMB(cbind(y,N-y) ~ 1, data=d, family=binomial())) m3 <- glmmTMB(y/N ~ 1, weights=N, data=d, family=binomial()) expect_equal(fixef(m0),fixef(m3)) m1 <- glmmTMB((y>5)~1,data=d,family=binomial) m2 <- glmmTMB(factor(y>5)~1,data=d,family=binomial) expect_equal(c(unname(logLik(m1))),-6.931472,tol=1e-6) expect_equal(c(unname(logLik(m2))),-6.931472,tol=1e-6) }) test_that("formula expansion", { ## test that formulas are expanded in the call/printed form <- Reaction ~ Days + (1|Subject) expect_equal(grep("Reaction ~ Days", capture.output(print(glmmTMB(form, sleepstudy))), fixed=TRUE),1) }) test_that("NA handling", { data(sleepstudy,package="lme4") ss <- sleepstudy ss$Days[c(2,20,30)] <- NA op <- options(na.action=NULL) expect_error(glmmTMB(Reaction~Days,ss),"missing values in object") op <- options(na.action=na.fail) expect_error(glmmTMB(Reaction~Days,ss),"missing values in object") expect_equal(unname(fixef(glmmTMB(Reaction~Days,ss,na.action=na.omit))[[1]]), c(249.70505,11.11263), tolerance=1e-6) op <- options(na.action=na.omit) expect_equal(unname(fixef(glmmTMB(Reaction~Days,ss))[[1]]), c(249.70505,11.11263), tolerance=1e-6) }) test_that("quine NB fit", { quine.nb1 <- MASS::glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine) quine.nb2 <- glmmTMB(Days ~ Sex/(Age + Eth*Lrn), data = quine, family=nbinom2()) expect_equal(coef(quine.nb1),fixef(quine.nb2)[["cond"]], tolerance=1e-4) }) ## quine.nb3 <- glmmTMB(Days ~ Sex + (1|Age), data = quine, ## family=nbinom2()) test_that("contrasts arg", { quine.nb1 <- MASS::glm.nb(Days ~ Sex*Age, data = quine, contrasts=list(Sex="contr.sum",Age="contr.sum")) quine.nb2 <- glmmTMB(Days ~ Sex*Age, data = quine, family=nbinom2(), contrasts=list(Sex="contr.sum",Age="contr.sum")) expect_equal(coef(quine.nb1),fixef(quine.nb2)[["cond"]], tolerance=1e-4) }) glmmTMB/tests/testthat/test-saveload.R0000644000176200001440000000070213614324717017460 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB")) context("Saving and loading glmmTMB objects") test_that("summary consistency", { data(sleepstudy, package="lme4") fm1 <- glmmTMB(Reaction ~ Days + (1|Subject), sleepstudy) s1 <- capture.output(print(summary(fm1))) save(fm1, file="fm1.Rdata") load("fm1.Rdata") file.remove("fm1.Rdata") s2 <- capture.output(print(summary(fm1))) expect_identical(s1, s2) }) glmmTMB/tests/testthat/test-offset.R0000644000176200001440000000411113614324717017146 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB")) context("offsets") set.seed(101) n <- 10000 mux <- 10 sdx <- 10 a <- .1 b <- 0 residsd <- .01 x <- rnorm(n, mux, sdx) o <<- 2*x+100 o2 <- rep(c(1,5),each=n/2) r1 <- rnorm(n, sd=residsd) r2 <- rnorm(n, sd=residsd*o2) y0 <- a*x + b + r1 y1 <- a*x+b+o+r1 y2 <- a*x+b+r2 y3 <- a*x + b + o + r2 ## global assignment for testthat dat <<- data.frame(y0, y1, y2, y3, x, o, o2, o3=o) m.lm <- lm(y1~x, offset=o, dat) m.lm0 <- lm(y1~x, dat) test_that("LM with offset as argument", { m1 <- glmmTMB(y1~x, offset=o, dat) expect_equal(fixef(m1)[[1]], coef(m.lm), tol=1e-6) m3 <- glmmTMB(y1~x, offset=o) expect_equal(fixef(m3)[[1]], coef(m.lm), tol=1e-6) }) test_that("LM with offset in formula", { m2 <- glmmTMB(y1~x+offset(o), dat) expect_equal(fixef(m2)[[1]], coef(m.lm), tol=1e-6) m4 <- glmmTMB(y1~x+offset(o)) expect_equal(fixef(m4)[[1]], coef(m.lm), tol=1e-6) }) ## test_that("LM with offset in zero-inflation formula", { ## don't have anything sensible to try here yet ... ## glmmTMB(y~x,zi=~1+offset(o), dat) ## }) test_that("LM with offset in formula - variable not in environment", { m5 <- glmmTMB(y1~x,offset=o3, dat) expect_equal(fixef(m5)[[1]],coef(m.lm), tol=1e-6) nullvalue <- NULL m6 <- glmmTMB(y1~x,offset=nullvalue, dat) expect_equal(fixef(m6)[[1]],coef(m.lm0), tol=1e-6) }) test_that("LM with offset in dispersion formula", { expect_equal(sigma(glmmTMB(y1~x, dat)), sigma(glmmTMB(y2~x,disp=~1+offset(log(o2)*2), dat)), tolerance=1e-3) }) test_that("LM with multiple offsets (cond/dispersion)", { m1 <<- glmmTMB(y0~x, dat) m2 <<- glmmTMB(y3~x+offset(o),disp=~1+offset(log(o2)*2), dat) expect_equal(sigma(m1),sigma(m2),tolerance=1e-3) expect_equal(fixef(m1),fixef(m2),tolerance=1e-3) }) ## this was broken by an earlier multiple-offset formulation test_that("LM with random crap in the formula", { m1 <<- glmmTMB(y0~dat$x, dat) m2 <<- glmmTMB(y0~x, dat) expect_equal(unname(fixef(m1)$cond),unname(fixef(m2)$cond)) }) glmmTMB/tests/testthat/test-control.R0000644000176200001440000000362513614324717017351 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB")) context("glmmTMBControl") ## Some selected L1-distances between two fits distFits <- function(fit1, fit2) { s1 <- summary(fit1) s2 <- summary(fit2) glmmTMB:::namedList( max(abs((coef(s1)$cond - coef(s2)$cond)[,"Estimate"])), max(abs((coef(s1)$cond - coef(s2)$cond)[,"Std. Error"])), abs(logLik(fit1) - logLik(fit2)) ) } test_that("profile method", { myfit <- function(...) { glmmTMB(count ~ mined * spp + (1|site), family = poisson, data = Salamanders, control = glmmTMBControl(...)) } m1 <- myfit( profile=FALSE ) m2 <- myfit( profile=TRUE ) expect_true( all( distFits(m1, m2) < c(1e-4, 1e-2, 1e-4) ) ) ## ########################################################### myfit <- function(...) { glmmTMB(count ~ mined * spp + (1|site), zi = ~ (1 | spp), family = poisson, data = Salamanders, control = glmmTMBControl(...)) } m1 <- myfit( profile=FALSE ) m2 <- myfit( profile=TRUE ) expect_true( all( distFits(m1, m2) < c(1e-4, 1e-2, 1e-4) ) ) }) test_that("parallel regions", { myfit <- function(...) { glmmTMB(count ~ mined * spp + (1|site), family = poisson, data = Salamanders, verbose = FALSE, control = glmmTMBControl(...)) } # Record time and model capture_time_model <- function(...) { start_time <- Sys.time() model <- myfit(...) end_time <- Sys.time() return(list(model = model, elapsed_time = end_time - start_time )) } m1 <- capture_time_model( parallel = 1 ) m2 <- capture_time_model( parallel = parallel::detectCores() ) expect_true( all( distFits(m1[[1]], m2[[1]]) < c(1e-4, 1e-2, 1e-4) ) ) # expect_true( m1[[2]] <= m2[[2]]) }) glmmTMB/tests/testthat/test-zi.R0000644000176200001440000000464613614324717016317 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB"), require("lme4")) ## simulate something smaller than the Owls data set? context("ZI models") data(Owls) test_that("zi", { ## Fit negative binomial model with "constant" Zero Inflation : owls_nb1 <<- glmmTMB(SiblingNegotiation ~ FoodTreatment*SexParent + (1|Nest)+offset(log(BroodSize)), family = nbinom1(), ziformula = ~1, data=Owls) owls_nb2 <<- update(owls_nb1, ziformula = ~ FoodTreatment*SexParent + (1|Nest)) owls_nb3 <<- update(owls_nb1,ziformula=~.) expect_equal(fixef(owls_nb2), structure(list(cond = structure(c(0.812028613585629, -0.342496105044418, -0.0751681324132088, 0.122484981295054), .Names = c("(Intercept)", "FoodTreatmentSatiated", "SexParentMale", "FoodTreatmentSatiated:SexParentMale")), zi = structure(c(-2.20863281353936, 1.86779027553285, -0.825200772653965, 0.451435813933449), .Names = c("(Intercept)", "FoodTreatmentSatiated", "SexParentMale", "FoodTreatmentSatiated:SexParentMale")), disp = structure(1.33089630005212, .Names = "(Intercept)")), .Names = c("cond", "zi", "disp"), class = "fixef.glmmTMB"), tolerance=1e-5) expect_equal(fixef(owls_nb2),fixef(owls_nb3)) }) test_that("zi beta and Gamma", { suppressWarnings(RNGversion("3.5.1")) set.seed(101) dd <- data.frame(yb=c(rbeta(100,shape1=2,shape2=1),rep(0,10)), yg=c(rgamma(100,shape=1.5,rate=1),rep(0,10))) expect_error(glmmTMB(yb~1, data=dd, family=beta_family), "y values must be") m1 <- glmmTMB(yb~1, data=dd, family=beta_family, zi=~1) expect_equal(unname(plogis(fixef(m1)[["zi"]])),1/11) expect_equal(unname(fixef(m1)[["cond"]]), 0.6211636, tolerance=1e-5) ## need *both* ziformula and family=ziGamma for gamma-hurdle expect_error(glmmTMB(yg~1, data=dd, family=Gamma), "non-positive values not allowed") expect_error(glmmTMB(yg~1, zi=~1, data=dd, family=Gamma), "non-positive values not allowed") expect_error(glmmTMB(yg~1, data=dd, family=ziGamma), "non-positive values not allowed") m2 <- glmmTMB(yg~1, data=dd, family=ziGamma(link="log"), zi=~1) expect_equal(unname(plogis(fixef(m2)[["zi"]])),1/11) expect_equal(unname(fixef(m2)[["cond"]]), 0.3995267, tolerance=1e-5) }) glmmTMB/tests/testthat/test-methods.R0000644000176200001440000003412313614324717017331 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB")) data(sleepstudy, cbpp, Pastes, package = "lme4") if (getRversion() < "3.3.0") { sigma.default <- function (object, use.fallback = TRUE, ...) sqrt(deviance(object, ...)/(nobs(object, use.fallback = use.fallback) - length(coef(object)))) } load(system.file("test_data", "models.rda", package="glmmTMB", mustWork=TRUE)) context("basic methods") test_that("Fitted and residuals", { expect_equal(length(fitted(fm2)),nrow(sleepstudy)) expect_equal(mean(fitted(fm2)),298.507891) expect_equal(mean(residuals(fm2)),0,tol=1e-5) ## Pearson and response are the same for a Gaussian model expect_equal(residuals(fm2,type="response"), residuals(fm2,type="pearson")) ## ... but not for Poisson or NB ... expect_false(mean(residuals(fm2P,type="response"))== mean(residuals(fm2P,type="pearson"))) expect_false(mean(residuals(fm2NB,type="response"))== mean(residuals(fm2NB,type="pearson"))) rr2 <- function(x) sum(residuals(x,type="pearson")^2) ## test Pearson resids for gaussian, Gamma vs. base-R versions ss <- as.data.frame(state.x77) expect_equal(rr2(glm(Murder~Population,ss,family=gaussian)), rr2(glmmTMB(Murder~Population,ss,family=gaussian))) expect_equal(rr2(glm(Murder~Population,ss,family=Gamma(link="log"))), rr2(glmmTMB(Murder~scale(Population),ss, family=Gamma(link="log"))),tol=1e-5) ## weights are incorporated in Pearson residuals ## GH 307 tmbm4 <- glm(incidence/size ~ period, data = cbpp, family = binomial, weights = size) tmbm5 <- glmmTMB(incidence/size ~ period, data = cbpp, family = binomial, weights = size) expect_equal(residuals(tmbm4,type="pearson"), residuals(tmbm5,type="pearson"),tolerance=1e-6) ## two-column responses give vector of residuals GH 307 tmbm6 <- glmmTMB(cbind(incidence,size-incidence) ~ period, data = cbpp, family = binomial) expect_equal(residuals(tmbm4,type="pearson"), residuals(tmbm6,type="pearson"),tolerance=1e-6) }) test_that("Predict", { expect_equal(predict(fm2),predict(fm2,newdata=sleepstudy)) pr2se <- predict(fm2, se.fit=TRUE) i <- sample(nrow(sleepstudy), 20) newdata <- sleepstudy[i, ] pr2sub <- predict(fm2, newdata=newdata, se.fit=TRUE) expect_equivalent(pr2se$fit, predict(fm2)) expect_equivalent(pr2se$fit[i], pr2sub$fit) expect_equivalent(pr2se$se.fit[i], pr2sub$se.fit) expect_equal(unname( pr2se$ fit[1] ), 254.2208, tol=1e-4) expect_equal(unname( pr2se$se.fit[1] ), 12.94514, tol=1e-4) expect_equal(unname( pr2se$ fit[100] ), 457.9684, tol=1e-4) expect_equal(unname( pr2se$se.fit[100] ), 14.13943, tol=1e-4) ## predict without response in newdata expect_equal(predict(fm2), predict(fm2,newdata=sleepstudy[,c("Days","Subject")])) }) test_that("VarCorr", { vv <- VarCorr(fm2) vv2 <- vv$cond$Subject expect_equal(dim(vv2),c(2,2)) expect_equal(outer(attr(vv2,"stddev"), attr(vv2,"stddev"))*attr(vv2,"correlation"), vv2,check.attributes=FALSE) vvd <- VarCorr(fm2diag) expect_equal(vvd$cond$Subject[1,2],0) ## off-diagonal==0 }) test_that("drop1", { dd <- drop1(fm2,test="Chisq") expect_equal(dd$AIC,c(1763.94,1785.48),tol=1e-4) }) test_that("anova", { aa <- anova(fm0,fm2) expect_equal(aa$AIC,c(1785.48,1763.94),tol=1e-4) }) test_that("terms", { ## test whether terms() are returned with predvars for doing ## model prediction etc. with complex bases dd <<- data.frame(x=1:10,y=1:10) require("splines") ## suppress convergence warnings(we know this is a trivial example) suppressWarnings(m <- glmmTMB(y~ns(x,3),dd)) ## if predvars is not properly attached to term, this will ## fail as it tries to construct a 3-knot spline from a single point expect_equal(model.matrix(delete.response(terms(m)),data=data.frame(x=1)), structure(c(1, 0, 0, 0), .Dim = c(1L, 4L), .Dimnames = list("1", c("(Intercept)", "ns(x, 3)1", "ns(x, 3)2", "ns(x, 3)3")), assign = c(0L, 1L, 1L, 1L))) }) test_that("terms back-compatibility", { f0 <- readRDS(system.file("test_data", "oldfit.rds", package="glmmTMB", mustWork=TRUE)) expect_true(!is.null(terms(f0))) }) test_that("summary_print", { getVal <- function(x,tag="Dispersion") { cc <- capture.output(print(summary(x))) if (length(gg <- grep(tag,cc,value=TRUE))==0) return(NULL) cval <- sub("^.*: ","",gg) ## get value after colon ... return(as.numeric(cval)) } ## no dispersion printed for Gaussian or disp==1 families expect_equal(getVal(fm2),654.9,tolerance=1e-2) expect_equal(getVal(fm2P),NULL) expect_equal(getVal(fm2G),0.00654,tolerance=1e-2) expect_equal(getVal(fm2NB,"Overdispersion"),286,tolerance=1e-2) }) test_that("sigma", { s1 <<- sigma(lm(Reaction~Days,sleepstudy)) s2 <<- sigma(glm(Reaction~Days,sleepstudy,family=Gamma(link="log"))) s3 <<- MASS::glm.nb(round(Reaction)~Days,sleepstudy) ## remove bias-correction expect_equal(sigma(fm3),s1*(1-1/nobs(fm3)),tolerance=1e-3) expect_equal(sigma(fm3G),s2,tolerance=5e-3) expect_equal(s3$theta,sigma(fm3NB),tolerance=1e-4) }) test_that("confint", { ci <- confint(fm2, 1:2, estimate=FALSE) expect_equal(ci, structure(c(238.406083254105, 7.52295734348693, 264.404107485727, 13.4116167530013), .Dim = c(2L, 2L), .Dimnames = list(c("(Intercept)", "Days"), c("2.5 %", "97.5 %"))), tolerance=1e-6) ciw <- confint(fm2, 1:2, method="Wald", estimate=FALSE) expect_warning(confint(fm2,type="junk"), "extra arguments ignored") ## Gamma test Std.Dev and sigma ci.2G <- confint(fm2G, full=TRUE, estimate=FALSE) ci.2G.expect <- structure(c(5.4810173444768, 0.0247781468857994, 0.0676097043327788, 0.0115949839191128, -0.518916570291726, 0.0720456818399729, 5.58401849115119, 0.0429217639222305, 0.150456372618643, 0.0264376535768207, 0.481694558481224, 0.0907365112123184), .Dim = c(6L, 2L), .Dimnames = list(c("cond.(Intercept)", "cond.Days", "cond.Std.Dev.(Intercept)", "cond.Std.Dev.Days", "cond.Cor.Days.(Intercept)", "sigma"), c("2.5 %", "97.5 %"))) expect_equal(ci.2G, ci.2G.expect, tolerance=1e-6) ## nbinom2 test Std.Dev and sigma ci.2NB <- confint(fm2NB, full=TRUE, estimate=FALSE) ci.2NB.expect <- structure(c(5.48098713986992, 0.0248163859092965, 0.066177247560203, 0.0113436356932709, -0.520883841816814, 183.810584738707, 5.58422550782448, 0.0428993227431795, 0.150917850214506, 0.026354988318893, 0.502211676507888, 444.735668635694), .Dim = c(6L, 2L), .Dimnames = list(c("cond.(Intercept)", "cond.Days", "cond.Std.Dev.(Intercept)", "cond.Std.Dev.Days", "cond.Cor.Days.(Intercept)", "sigma"), c("2.5 %", "97.5 %"))) expect_equal(ci.2NB, ci.2NB.expect, tolerance=1e-6) ## profile CI ## ... no RE ci.prof0 <- confint(fm_noRE, full=TRUE, method="profile", npts=3) expect_equal(ci.prof0, structure(c(238.216039176535, 7.99674863649355, 7.51779308310198, 264.368471102549, 12.8955469713508, 7.93347860201449), .Dim = 3:2, .Dimnames = list(c("(Intercept)", "Days", "d~(Intercept)"), c("2.5 %", "97.5 %"))), tolerance=1e-5) ci.prof <- confint(fm2,parm=1,method="profile", npts=3) expect_equal(ci.prof, structure(c(237.27249, 265.13383), .Dim = 1:2, .Dimnames = list( "(Intercept)", c("2.5 %", "97.5 %"))), tolerance=1e-6) ## uniroot CI ci.uni <- confint(fm2,parm=1,method="uniroot") expect_equal(ci.uni, structure(c(237.68071,265.12949,251.4050979), .Dim = c(1L, 3L), .Dimnames = list("(Intercept)", c("2.5 %", "97.5 %", "Estimate"))), tolerance=1e-6) ## check against 'raw' tmbroot tmbr <- TMB::tmbroot(fm2$obj,name=1) expect_equal(ci.uni[1:2],unname(c(tmbr))) ## GH #438 cc <- confint(fm4) expect_equal(dim(cc),c(5,3)) expect_equal(rownames(cc), c("(Intercept)", "Illiteracy", "Population", "Area", "`HS Grad`")) }) test_that("profile", { p1_th <- profile(fm1,parm="theta_",npts=4) expect_true(all(p1_th$.par=="theta_1|Subject.1")) p1_b <- profile(fm1,parm="beta_",npts=4) expect_equal(unique(as.character(p1_b$.par)), c("(Intercept)","Days")) }) test_that("profile (no RE)", { p0_th <- profile(fm_noRE,npts=4) expect_equal(dim(p0_th),c(43,3)) }) test_that("vcov", { expect_equal(dim(vcov(fm2)[[1]]),c(2,2)) expect_equal(dim(vcov(fm2,full=TRUE)),c(6,6)) expect_equal(rownames(vcov(fm2,full=TRUE)), structure(c("(Intercept)", "Days", "d~(Intercept)", "theta_Days|Subject.1", "theta_Days|Subject.2", "theta_Days|Subject.3"), .Names = c("cond1", "cond2", "disp", "theta1", "theta2", "theta3"))) ## vcov doesn't include dispersion for non-dispersion families ... expect_equal(dim(vcov(fm2P,full=TRUE)),c(5,5)) }) set.seed(101) test_that("simulate", { sm2 <<- rowMeans(do.call(cbind, simulate(fm2, 10))) sm2P <<- rowMeans(do.call(cbind, simulate(fm2P, 10))) sm2G <<- rowMeans(do.call(cbind, simulate(fm2G, 10))) sm2NB <<- rowMeans(do.call(cbind, simulate(fm2NB, 10))) expect_equal(sm2, sleepstudy$Reaction, tol=20) expect_equal(sm2P, sleepstudy$Reaction, tol=20) expect_equal(sm2G, sleepstudy$Reaction, tol=20) expect_equal(sm2NB, sleepstudy$Reaction, tol=20) }) test_that("formula", { expect_equal(formula(fm2),Reaction ~ Days + (Days | Subject)) expect_equal(formula(fm2, fixed.only=TRUE),Reaction ~ Days) expect_equal(formula(fm2, component="disp"), ~1) expect_equal(formula(fm2, component="disp", fixed.only=TRUE), ~1) expect_equal(formula(fm2, component="zi"), ~0) expect_equal(formula(fm2, component="zi", fixed.only=TRUE), ~0) }) context("simulate consistency with glm/lm") test_that("binomial", { s1 <- simulate(f1b, 5, seed=1) s2 <- simulate(f2b, 5, seed=1) s3 <- simulate(f3b, 5, seed=1) expect_equal(max(abs(as.matrix(s1) - as.matrix(s2))), 0) expect_equal(max(abs(as.matrix(s1) - as.matrix(s3))), 0) }) test_that("residuals from binomial factor responses", { expect_equal(residuals(fm2Bf),residuals(fm2Bn)) }) mkstr <- function(dd) { ff <- which(vapply(dd,is.factor,logical(1))) for (i in ff) { dd[[i]] <- as.character(dd[[i]]) } return(dd) } rr <- function(txt) { read.table(header=TRUE,stringsAsFactors=FALSE,text=txt, colClasses=rep(c("character","numeric"),c(5,2))) } context("Ranef etc.") test_that("as.data.frame(ranef(.)) works", { expect_equal( mkstr(as.data.frame(ranef(fm3ZIP))[c("cond.1","cond.19","zi.1"),]), rr( " component grpvar term grp condval condsd cond.1 cond Subject (Intercept) 308 1.066599e-02 0.040430751 cond.19 cond Subject Days 308 2.752424e-02 0.007036958 zi.1 zi Subject (Intercept) 308 -2.850238e-07 0.127106817 "), tolerance=1e-5) expect_equal( mkstr(as.data.frame(ranef(fm2diag2))[c("cond.1","cond.19"),]), rr( " component grpvar term grp condval condsd cond.1 cond Subject (Intercept) 308 1.854597 13.294388 cond.19 cond Subject Days 308 9.236420 2.699692 "), tolerance=1e-5) }) test_that("ranef(.) works with more than one grouping factor", { expect_equal(sort(names(ranef(fmP)[["cond"]])), c("batch","sample")) expect_equal(dim(as.data.frame(ranef(fmP))), c(40,6)) }) test_that("coef(.) works", { cc <- coef(fm3ZIP) expect_equal(cc[["cond"]][[1]][1,], structure(list(`(Intercept)` = 5.54291514202372, Days = 0.0613847280572168), row.names = "308", class = "data.frame"), tolerance=1e-5) expect_equal(cc[["zi"]][[1]][1,,drop=FALSE], structure(list(`(Intercept)` = -13.2478200379555), row.names = "308", class = "data.frame"), tolerance=1e-5) }) test_that("simplified coef(.) printing", { op <- options(digits=2) cc <- capture.output(print(coef(fm0))) expect_equal(cc[1:3],c("$Subject", " Days (Intercept)", "308 20.6 249")) options(op) }) context("refit") ## weird stuff here with environments, testing ... test_that("various binomial response types work", { ## FIXME: test for factors, explicit cbind(.,.) ## do we need to define this within this scope? ddb <- data.frame(y=I(yb)) ddb <- within(ddb, { w <- rowSums(yb) prop <- y[,1]/w }) s1 <- simulate(f1b, 1, seed=1) f1 <- fixef(lme4::refit(f1b,s1[[1]])) s3 <- simulate(f3b, 1, seed=1) f3 <- fixef(lme4::refit(f3b,s3[[1]])) expect_equal(f1,f3) expect_error(lme4::refit(f4b,s3[[1]]), "can't find response in data") }) test_that("binomial response types work with data in external scope", { s1 <- simulate(f1b, 1, seed=1) f1 <- fixef(lme4::refit(f1b,s1[[1]])) s3 <- simulate(f3b, 1, seed=1) f3 <- fixef(lme4::refit(f3b,s3[[1]])) expect_equal(f1,f3) }) glmmTMB/tests/testthat/test-utils.R0000644000176200001440000000031713614324717017024 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB")) test_that("addForm", { expect_equal(addForm(y~x,~1,~z),y~x+1+z) expect_warning(addForm(y~x,z~1), "discarding LHS") }) glmmTMB/tests/testthat/test-downstream.R0000644000176200001440000000726213616054060020046 0ustar liggesusersrequire(glmmTMB) require(testthat) data(sleepstudy,package="lme4") if (require(emmeans)) { context("emmeans") m1 <- glmmTMB(SiblingNegotiation ~ FoodTreatment*SexParent + (1|Nest)+offset(log(BroodSize)), family = nbinom1(), zi = ~1, data=Owls) em1 <- emmeans(m1, poly ~ FoodTreatment | SexParent) em2 <- emmeans(m1, poly ~ FoodTreatment | SexParent, type = "response") expect_is(em1,"emm_list") expect_true(any(grepl("given on the log (not the response) scale", capture.output(print(em1)),fixed=TRUE))) expect_true(any(grepl("back-transformed from the log scale", capture.output(print(em2))))) expect_equal(summary(em1[[2]])$estimate[1], -0.8586306, tolerance=1e-4) expect_equal(summary(em2[[2]])$ratio[1], 0.42374, tolerance=1e-4) m2 <- glmmTMB(count ~ spp + mined + (1|site), zi=~spp + mined, family=nbinom2, data=Salamanders) rgc <- ref_grid(m2, component = "cond") expect_is(rgc, "emmGrid") expect_equal(predict(rgc)[2], -1.574079, tolerance=1e-4) expect_equal(predict(rgc, type="response")[2], 0.207198, tolerance=1e-4) rgz <- ref_grid(m2, component = "zi") expect_is(rgz, "emmGrid") expect_equal(predict(rgz)[2], 2.071444, tolerance=1e-4) expect_equal(predict(rgz, type="response")[2], 0.88809654, tolerance=1e-4) } if (require(car) && getRversion()>="3.6.0") { ## only testing on recent R: see comments ## https://github.com/glmmTMB/glmmTMB/pull/547#issuecomment-580690208 ## https://github.com/glmmTMB/glmmTMB/issues/493#issuecomment-578569564 context("car::Anova") fm1 <- glmmTMB(Reaction~Days+(1|Subject),sleepstudy) ## lme4 is imported so we don't need to explicitly require() it fm0 <- lme4::lmer(Reaction~Days+(1|Subject),sleepstudy,REML=FALSE) expect_equal(Anova(fm1),Anova(fm0),tolerance=3e-6) expect_equal(Anova(fm1,type="III"),Anova(fm0,type="III"),tolerance=3e-6) ## test Anova on various components fmd <- glmmTMB(Reaction~Days+(1|Subject), disp=~I(Days>5), sleepstudy, REML=FALSE) ad <- Anova(fmd,component="disp") expect_equal(ad[1,1],18.767,tolerance=1e-5) expect_equal(rownames(ad), "I(Days > 5)") ac <- Anova(fmd,component="cond") expect_equal(ac[1,1], 160.1628, tolerance=1e-5) expect_equal(rownames(ac), "Days") expect_error(Anova(fmd,component="zi"), "trivial fixed effect") } if (require(effects)) { context("effects") ## pass dd: some kind of scoping issue in testthat context f <- function(x,dd) { sapply(allEffects(x), function(y) { y$transformation$inverse(y$fit) }) } fm2_tmb <- glmmTMB(round(Reaction)~Days+(1|Subject),family=poisson,data=sleepstudy) fm2_lmer <- lme4::glmer(round(Reaction)~Days+(1|Subject),family=poisson,data=sleepstudy) if (getRversion() >= "3.6.0") { ## only testing on recent R: see comments ## https://github.com/glmmTMB/glmmTMB/pull/547#issuecomment-580690208 ## https://github.com/glmmTMB/glmmTMB/issues/493#issuecomment-578569564 expect_equal(f(fm2_tmb),f(fm2_lmer),tolerance=2e-5) ## set.seed(101) dd <<- data.frame(y=rnbinom(1000,mu=4,size=1), x = rnorm(1000), f=factor(rep(LETTERS[1:20],each=50))) fm3_tmb <- glmmTMB(y~x,family=nbinom2,data=dd) fm3_MASS <- MASS::glm.nb(y~x,data=dd) ## suppressing "overriding variance function for effects: computed variances may be incorrect" warning here expect_equal(suppressWarnings(f(fm3_tmb,dd)),f(fm3_MASS,dd),tolerance=2e-5) } ## recent R } ## effects glmmTMB/tests/testthat/test-families.R0000644000176200001440000003522213614324717017460 0ustar liggesusers## test more exotic familes/model types stopifnot(require("testthat"), require("glmmTMB")) simfun0 <- function(beta=c(2,1), sd.re=5, ngrp=10,nobs=200, invlink=exp) { x <- rnorm(nobs) f <- factor(rep(1:ngrp,nobs/ngrp)) u <- rnorm(ngrp,sd=sd.re) eta <- beta[1]+beta[2]*x+u[f] mu <- invlink(eta) return(data.frame(x,f,mu)) } context("alternative binomial specifications") test_that("binomial", { load(system.file("testdata","radinger_dat.RData",package="lme4")) radinger_dat <<- radinger_dat ## global assignment for testthat mod1 <<- glmmTMB(presabs~predictor+(1|species),family=binomial, radinger_dat) mod2 <<- update(mod1,as.logical(presabs)~.) expect_equal(predict(mod1),predict(mod2)) ## Compare 2-column and prop/size specification dd <- data.frame(success=1:10, failure=11:20) dd$size <- rowSums(dd) dd$prop <- local( success / size, dd) mod4 <- glmmTMB(cbind(success,failure)~1,family=binomial,data=dd) mod5 <- glmmTMB(prop~1,weights=size,family=binomial,data=dd) expect_equal( logLik(mod4) , logLik(mod5) ) expect_equal( fixef(mod4)$cond , fixef(mod5)$cond ) ## Now with extra weights dd$w <- 2 mod6 <- glmmTMB(cbind(success,failure)~1,family=binomial,data=dd,weights=w) mod7 <- glmmTMB(prop~1,weights=size*w,family=binomial,data=dd) mod6.glm <- glm(cbind(success,failure)~1,family=binomial,data=dd,weights=w) mod7.glm <- glm(prop~1,weights=size*w,family=binomial,data=dd) expect_equal( logLik(mod6)[[1]] , logLik(mod6.glm)[[1]] ) expect_equal( logLik(mod7)[[1]] , logLik(mod7.glm)[[1]] ) expect_equal( fixef(mod6)$cond , fixef(mod7)$cond ) ## Test TRUE/FALSE specification x <- c(TRUE, TRUE, FALSE) m1 <- glmmTMB(x~1, family=binomial()) m2 <- glm (x~1, family=binomial()) expect_equal( as.numeric(logLik(m1)), as.numeric(logLik(m2)) ) expect_equal( as.numeric(unlist(fixef(m1))), as.numeric(coef(m2)) ) ## Mis-specifications prop <- c(.1, .2, .3) ## weights=1 => prop * weights non integers expect_warning( glmmTMB(prop~1, family=binomial()) ) ## Warning as glm x <- c(1, 2, 3) ## weights=1 => x > weights ! expect_error ( glmmTMB(x~1, family=binomial()) ) ## Error as glm }) context("non-integer count warnings") test_that("count distributions", { dd <- data.frame(y=c(0.5,1,1,1)) for (f in c("binomial","betabinomial","poisson", "genpois", ## "compois", ## fails anyway ... "truncated_genpois", # "truncated_compois", "nbinom1", "nbinom2" # why do these truncated cases fail? ##, "truncated_nbinom1", ##"truncated_nbinom2" )) { expect_warning(m <- glmmTMB(y~1,data=dd,family=f), "non-integer") } }) context("fitting exotic families") test_that("beta", { set.seed(101) nobs <- 200; eps <- 0.001; phi <- 0.1 dd0 <- simfun0(nobs=nobs,sd.re=1,invlink=plogis) y <- with(dd0,rbeta(nobs,shape1=mu/phi,shape2=(1-mu)/phi)) dd <<- data.frame(dd0,y=pmin(1-eps,pmax(eps,y))) m1 <- glmmTMB(y~x+(1|f),family=beta_family(), data=dd) expect_equal(fixef(m1)[[1]], structure(c(1.98250567574413, 0.843382531038295), .Names = c("(Intercept)", "x")), tol=1e-5) expect_equal(c(VarCorr(m1)[[1]][[1]]), 0.433230926800709, tol=1e-5) ## allow family="beta", but with warning expect_warning(m2 <- glmmTMB(y~x+(1|f),family="beta", data=dd),"please use") expect_equal(coef(summary(m1)),coef(summary(m2))) }) test_that("nbinom", { nobs <- 200; phi <- 0.1 set.seed(101) dd0 <- simfun0(nobs=nobs) ## global assignment for testthat (??) dd <- data.frame(dd0,y=rnbinom(nobs,size=phi,mu=dd0$mu)) m1 <- glmmTMB(y~x+(1|f),family=nbinom2(), data=dd) expect_equal(fixef(m1)[[1]], structure(c(2.09866748794435, 1.12703589660625), .Names = c("(Intercept)", "x")), tol=1e-5) expect_equal(c(VarCorr(m1)[[1]][[1]]), 9.54680210862774, tol=1e-5) expect_equal(sigma(m1),0.09922738,tol=1e-5) ## nbinom1 ## to simulate, back-calculate shape parameters for NB2 ... nbphi <- 2 nbvar <- nbphi*dd0$mu ## n.b. actual model is (1+phi)*var, ## so estimate of phi is approx. 1 ## V = mu*(1+mu/k) -> mu/k = V/mu-1 -> k = mu/(V/mu-1) k <- with(dd0,mu/(nbvar/mu - 1)) y <- rnbinom(nobs,size=k,mu=dd$mu) dd <- data.frame(dd0,y=y) ## global assignment for testthat m1 <- glmmTMB(y~x+(1|f),family=nbinom1(), data=dd) expect_equal(c(unname(c(fixef(m1)[[1]])), c(VarCorr(m1)[[1]][[1]]), sigma(m1)), c(1.93154240357181, 0.992776302432081, 16.447888398429, 1.00770603513152), tol=1e-5) ## identity link: GH #20 x <- 1:100; m <- 2; b <- 100 y <- m*x+b set.seed(101) dat <<- data.frame(obs=rnbinom(length(y), mu=y, size=5), x=x) ## with(dat, plot(x, obs)) ## coef(mod1 <- MASS::glm.nb(obs~x,link="identity",dat)) expect_equal(fixef(glmmTMB(obs~x, family=nbinom2(link="identity"), dat)), structure(list(cond = structure(c(115.092240041138, 1.74390840106971), .Names = c("(Intercept)", "x")), zi = numeric(0), disp = structure(1.71242627201796, .Names = "(Intercept)")), .Names = c("cond", "zi", "disp"), class = "fixef.glmmTMB")) ## segfault (GH #248) dd <- data.frame(success=1:10,failure=10) expect_error(glmmTMB(cbind(success,failure)~1,family=nbinom2,data=dd), "matrix-valued responses are not allowed") }) test_that("dbetabinom", { set.seed(101) nobs <- 200; eps <- 0.001; phi <- 0.1 dd0 <- simfun0(nobs=nobs,sd.re=1,invlink=plogis) p <- with(dd0,rbeta(nobs,shape1=mu/phi,shape2=(1-mu)/phi)) p <- pmin(1-eps,pmax(p,eps)) b <- rbinom(nobs,size=5,prob=p) dd <<- data.frame(dd0,y=b,N=5) m1 <- glmmTMB(y/N~x+(1|f), weights=N, family=betabinomial(), data=dd) expect_equal(c(unname(c(fixef(m1)[[1]])), c(VarCorr(m1)[[1]][[1]]), sigma(m1)), c(2.1482114,1.0574946,0.7016553,8.3768711), tolerance=1e-5) ## Two-column specification m2 <- glmmTMB(cbind(y, N-y) ~ x + (1|f), family=betabinomial(), data=dd) expect_identical(m1$fit, m2$fit) }) test_that("truncated", { ## Poisson set.seed(101) z_tp <<- rpois(1000,lambda=exp(1)) z_tp <<- z_tp[z_tp>0] if (FALSE) { ## n.b.: keep library() calls commented out, they may ## trigger CRAN complaints ## library(glmmADMB) g0_tp <- glmmadmb(z_tp~1,family="truncpoiss",link="log") fixef(g0) ## 0.9778591 } g1_tp <- glmmTMB(z_tp~1,family=truncated_poisson(), data=data.frame(z_tp)) expect_equal(unname(fixef(g1_tp)[[1]]),0.9778593,tol=1e-5) ## Truncated poisson with zeros => invalid: num_zeros <- 10 z_tp0 <<- c(rep(0, num_zeros), z_tp) expect_error(g1_tp0 <- glmmTMB(z_tp0~1,family=truncated_poisson(), data=data.frame(z_tp0))) ## Truncated poisson with zeros and zero-inflation: g1_tp0 <- glmmTMB(z_tp0~1,family=truncated_poisson(), ziformula=~1, data=data.frame(z_tp0)) expect_equal( plogis(as.numeric(fixef(g1_tp0)$zi)), num_zeros/length(z_tp0), tol=1e-7 ) ## Test zero-prob expect_equal(fixef(g1_tp0)$cond, fixef(g1_tp)$cond, tol=1e-6) ## Test conditional model ## nbinom2 set.seed(101) z_nb <<- rnbinom(1000,size=2,mu=exp(2)) z_nb <<- z_nb[z_nb>0] if (FALSE) { ## library(glmmADMB) g0_nb2 <- glmmadmb(z_nb~1,family="truncnbinom",link="log") fixef(g0_nb2) ## 1.980207 g0_nb2$alpha ## 1.893 } g1_nb2 <- glmmTMB(z_nb~1,family=truncated_nbinom2(), data=data.frame(z_nb)) expect_equal(c(unname(fixef(g1_nb2)[[1]]),sigma(g1_nb2)), c(1.980207,1.892970),tol=1e-5) ## Truncated nbinom2 with zeros => invalid: num_zeros <- 10 z_nb0 <<- c(rep(0, num_zeros), z_nb) expect_error(g1_nb0 <- glmmTMB(z_nb0~1,family=truncated_nbinom2(), data=data.frame(z_nb0))) ## Truncated nbinom2 with zeros and zero-inflation: g1_nb0 <- glmmTMB(z_nb0~1,family=truncated_nbinom2(), ziformula=~1, data=data.frame(z_nb0)) expect_equal( plogis(as.numeric(fixef(g1_nb0)$zi)), num_zeros/length(z_nb0), tol=1e-7 ) ## Test zero-prob expect_equal(fixef(g1_nb0)$cond, fixef(g1_nb2)$cond, tol=1e-6) ## Test conditional model ## nbinom1: constant mean, so just a reparameterization of ## nbinom2 (should have the same likelihood) ## phi=(1+mu/k)=1+exp(2)/2 = 4.69 if (FALSE) { ## library(glmmADMB) g0_nb1 <- glmmadmb(z_nb~1,family="truncnbinom1",link="log") fixef(g0_nb1) ## 2.00112 g0_nb1$alpha ## 3.784 } g1_nb1 <- glmmTMB(z_nb~1,family=truncated_nbinom1(), data=data.frame(z_nb)) expect_equal(c(unname(fixef(g1_nb1)[[1]]),sigma(g1_nb1)), c(1.980207,3.826909),tol=1e-5) ## Truncated nbinom1 with zeros => invalid: expect_error(g1_nb0 <- glmmTMB(z_nb0~1,family=truncated_nbinom1(), data=data.frame(z_nb0))) ## Truncated nbinom2 with zeros and zero-inflation: g1_nb0 <- glmmTMB(z_nb0~1,family=truncated_nbinom1(), ziformula=~1, data=data.frame(z_nb0)) expect_equal( plogis(as.numeric(fixef(g1_nb0)$zi)), num_zeros/length(z_nb0), tol=1e-7 ) ## Test zero-prob expect_equal(fixef(g1_nb0)$cond, fixef(g1_nb1)$cond, tol=1e-6) ## Test conditional model }) ##Genpois test_that("truncated_genpois",{ tgp1 <<- glmmTMB(z_nb ~1, data=data.frame(z_nb), family=truncated_genpois()) tgpdat <<- data.frame(y=simulate(tgp1)[,1]) tgp2 <<- glmmTMB(y ~1, tgpdat, family=truncated_genpois()) expect_equal(sigma(tgp1), sigma(tgp2), tol=1e-1) expect_equal(fixef(tgp1)$cond[1], fixef(tgp2)$cond[1], tol=1e-2) cc <- confint(tgp2, full=TRUE) expect_lt(cc["sigma", "2.5 %"], sigma(tgp1)) expect_lt(sigma(tgp1), cc["sigma", "97.5 %"]) expect_lt(cc["cond.(Intercept)", "2.5 %"], unname(fixef(tgp1)$cond[1])) expect_lt(unname(fixef(tgp1)$cond[1]), cc["cond.(Intercept)", "97.5 %"]) }) context("trunc compois") ##Compois test_that("truncated_compois",{ cmpdat <<- data.frame(f=factor(rep(c('a','b'), 10)), y=c(15,5,20,7,19,7,19,7,19,6,19,10,20,8,21,8,22,7,20,8)) tcmp1 <<- glmmTMB(y~f, cmpdat, family= truncated_compois()) expect_equal(unname(fixef(tcmp1)$cond), c(2.9652730653, -0.9773987194), tol=1e-6) expect_equal(sigma(tcmp1), 0.1833339, tol=1e-6) expect_equal(predict(tcmp1,type="response")[1:2], c(19.4, 7.3), tol=1e-6) }) context("compois") test_that("compois", { # cmpdat <<- data.frame(f=factor(rep(c('a','b'), 10)), # y=c(15,5,20,7,19,7,19,7,19,6,19,10,20,8,21,8,22,7,20,8)) cmp1 <<- glmmTMB(y~f, cmpdat, family=compois()) expect_equal(unname(fixef(cmp1)$cond), c(2.9652730653, -0.9773987194), tol=1e-6) expect_equal(sigma(cmp1), 0.1833339, tol=1e-6) expect_equal(predict(cmp1,type="response")[1:2], c(19.4, 7.3), tol=1e-6) }) context("genpois") test_that("genpois", { gendat <<- data.frame(y=c(11,10,9,10,9,8,11,7,9,9,9,8,11,10,11,9,10,7,13,9)) gen1 <<- glmmTMB(y~1, family=genpois(), gendat) expect_equal(unname(fixef(gen1)$cond), 2.251292, tol=1e-6) expect_equal(sigma(gen1), 0.235309, tol=1e-6) }) context("tweedie") test_that("tweedie", { ## Boiled down tweedie:::rtweedie : rtweedie <- function (n, xi = power, mu, phi, power = NULL) { mu <- array(dim = n, mu) if ((power > 1) & (power < 2)) { rt <- array(dim = n, NA) lambda <- mu^(2 - power)/(phi * (2 - power)) alpha <- (2 - power)/(1 - power) gam <- phi * (power - 1) * mu^(power - 1) N <- rpois(n, lambda = lambda) for (i in (1:n)) { rt[i] <- sum(rgamma(N[i], shape = -alpha, scale = gam[i])) } } else stop() as.vector(rt) } ## Simulation experiment nobs <- 2000; mu <- 4; phi <- 2; p <- 1.7 set.seed(101) y <- rtweedie(nobs, mu=mu, phi=phi, power=p) twm <- glmmTMB(y ~ 1, family=tweedie()) ## Check mu expect_equal(unname( exp(fixef(twm)$cond) ), mu, tolerance = .1) ## Check phi expect_equal(unname( exp(fixef(twm)$disp) ), phi, tolerance = .1) ## Check power expect_equal(unname( plogis(twm$fit$par["thetaf"]) + 1 ), p, tolerance = .01) ## Check internal rtweedie used by simulate y2 <- c(simulate(twm)[,1],simulate(twm)[,1]) twm2 <- glmmTMB(y2 ~ 1, family=tweedie()) expect_equal(fixef(twm)$cond, fixef(twm2)$cond, tol=1e-1) expect_equal(sigma(twm), sigma(twm2), tol=1e-1) }) test_that("gaussian_sqrt", { set.seed(101) nobs <- 200 dd0_sqrt <- simfun0(nobs=nobs,sd.re=1,invlink=function(x) x^2) dd0_sqrt$y <- rnorm(nobs,mean=dd0_sqrt$mu,sd=0.1) g1 <- glmmTMB(y~x+(1|f), family=gaussian(link="sqrt"), data=dd0_sqrt) expect_equal(fixef(g1), structure(list(cond = c(`(Intercept)` = 2.03810165917618, x = 1.00241002916226 ), zi = numeric(0), disp = c(`(Intercept)` = -4.68350239019746)), class = "fixef.glmmTMB"), tol=1e-6) }) context("link function info available") fam1 <- c("poisson","nbinom1","nbinom2","compois") fam2 <- c("binomial","beta_family","betabinomial","tweedie") for (f in c(fam1,paste0("truncated_",fam1),fam2)) { ## print(f) expect_true("linkinv" %in% names(get(f)())) } context("link info added to family") d.AD <- data.frame(counts=c(18,17,15,20,10,20,25,13,12), outcome=gl(3,1,9), treatment=gl(3,3)) glm.D93 <- glmmTMB(counts ~ outcome + treatment, family = poisson(), d.AD) expect_warning(glm.D93B <- glmmTMB(counts ~ outcome + treatment, family = list(family="poisson", link="log"), d.AD)) ## note update(..., family= ...) is only equal up to tolerance=5e-5 ... glm.D93C <- glmmTMB(counts ~ outcome + treatment, family = "poisson", d.AD) expect_equal(predict(glm.D93),predict(glm.D93B)) expect_equal(predict(glm.D93),predict(glm.D93C)) glmmTMB/tests/testthat/test-weight.R0000644000176200001440000000407113614324717017154 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB"), require("MASS")) context("weight") set.seed(1) nrep <- 20 nsim <- 5 sdi <- .1 sdii <- .2 rho <- -.1 slope <- .8 ni<-100 dat <- expand.grid(i=1:ni, rep=1:nrep , x=c(0 ,.2, .4)) RE <- MASS::mvrnorm(n = ni, mu =c(0, 0), Sigma = matrix(c(sdi*sdi, rho*sdi*sdii, rho*sdi*sdii ,sdii*sdii),2,2)) inddat <- transform(dat, y=rpois(n=nrow(dat), lambda = exp(RE[i,1] + x*(slope + RE[i,2])))) ## aggdat = ddply(inddat, ~i+x+y, summarize, freq=length(rep)) aggdat <- with(inddat,as.data.frame(table(i,x,y), stringsAsFactors=FALSE)) aggdat <- aggdat[with(aggdat,order(i,x,y)),] ## cosmetic/match previous aggdat <- subset(aggdat,Freq>0) ## drop zero categories aggdat <- transform(aggdat, i=as.integer(i), x=as.numeric(x), y=as.numeric(y)) ## only difference from previous is name of weights arg (Freq vs freq) test_that("Weights can be an argument", { wei_glmmtmb <<- glmmTMB(y ~ x+(x|i), data=aggdat, weight=Freq, family="poisson") expect_equal(unname(fixef(wei_glmmtmb)$cond), c(-0.00907013282660578, 0.944062427131668), tolerance=1e-6) }) test_that("Return weights", { expect_equal(weights(wei_glmmtmb), aggdat$Freq) expect_equal(weights(wei_glmmtmb, type="prior"), aggdat$Freq) ## partial matching expect_equal(weights(wei_glmmtmb, type="prio"), aggdat$Freq) expect_error(weights(wei_glmmtmb, type = "working"),"should be one of") expect_warning(weights(wei_glmmtmb, junk = "abc"), "unused arguments ignored") }) ind_glmmtmb <<- glmmTMB(y ~ x+(x|i), data=inddat, family="poisson") test_that("Estimates are the same", { expect_equal(summary(wei_glmmtmb)$coefficients$cond, summary(ind_glmmtmb)$coefficients$cond, tolerance=1e-6) expect_equal(ranef(wei_glmmtmb), ranef(ind_glmmtmb), tolerance=1e-5) expect_equal(AIC(wei_glmmtmb), AIC(ind_glmmtmb), tolerance=1e-5) }) glmmTMB/tests/testthat/test-start.R0000644000176200001440000000100413614324717017013 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB")) data(sleepstudy, cbpp, package = "lme4") test_that("error messages for user-spec start", { expect_error( glmmTMB(Reaction~Days+(Days|Subject), sleepstudy, start=list(beta=c(2))), "parameter vector length mismatch.*length\\(beta\\)==1, should be 2") expect_error(glmmTMB(Reaction~Days+(Days|Subject), sleepstudy, start=list(junk=5)), "unrecognized vector 'junk'") }) glmmTMB/tests/testthat/test-formulas.R0000644000176200001440000000267213614324717017522 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB")) context("formula parsing") nrt <- function(x) length(x$reTrmFormulas) test_that("basic splitForm", { expect_equal(nrt(splitForm(y~(x+q))),0) ## reTrms part should be empty sf1 <- splitForm(y~(x+q)+(1|f)) sf2 <- splitForm(y~(x+q)+us(1|f)) sf3 <- splitForm(y~(x+q)+diag(1|f)) sf4 <- splitForm(~x+y+(f|g)+cs(1|g)) expect_equal(nrt(sf1),1) expect_equal(sf1$reTrmFormulas,list(quote(1|f))) expect_equal(sf1,sf2) expect_equal(sf3$reTrmClasses,"diag") expect_equal(sf4$reTrmClasses,c("us","cs")) }) test_that("slash terms", { sf5 <- splitForm(~x+y+(1|f/g)) sf6 <- splitForm(~x+y+(1|f/g/h)) sf7 <- splitForm(~x+y+(1|(f/g)/h)) expect_equal(sf5$reTrmClasses,rep("us",2)) expect_equal(sf6$reTrmClasses,rep("us",3)) expect_equal(sf6,sf7) }) test_that("grpvar terms", { sf8 <- splitForm(~x+y+(1|f*g)) sf9 <- splitForm(~x+y+(1|f+g+h)) expect_equal(sf8$reTrmClasses,rep("us",3)) expect_equal(sf8$reTrmFormula,list(quote(1|f),quote(1|g),quote(1|f:g))) expect_equal(sf9$reTrmClasses,rep("us",3)) expect_equal(sf9$reTrmFormula,list(quote(1|f),quote(1|g),quote(1|h))) }) test_that("noSpecial", { ## handle parentheses in formulas: GH #174 ff <- y~1+(((us(1|f)))) expect_equal(noSpecials(ff,delete=FALSE),y~1+(1|f)) expect_equal(noSpecials(ff),y~1) ## 'naked' special - left alone: GH #261 ff2 <- y ~ us expect_equal(noSpecials(ff2),ff2) }) glmmTMB/tests/testthat/test-bootMer.R0000644000176200001440000000152613614324717017276 0ustar liggesusersstopifnot(require("testthat"), require("glmmTMB"), require("lme4")) context("bootMer") fun <- function(x) predict(x)[1] test_that("Bernoulli responses", { Salamanders$pres <- as.numeric(Salamanders$count>0) m <- glmmTMB(pres ~ mined +(1|site), family=binomial, data=Salamanders) b <- lme4::bootMer(m, fun, nsim=2, seed=101) expect_true(var(c(b$t))>0) expect_equal(suppressWarnings(c(confint(b))), c(-1.579923,-1.250725),tolerance=1e-5) }) test_that("Bernoulli responses", { m <- glmmTMB(count ~ mined + (1|site), family=poisson, data=Salamanders) ss1 <- simulate(m,nsim=2,seed=101) b <- bootMer(m, fun, nsim=2, seed=101) expect_true(var(c(b$t))>0) expect_equal(suppressWarnings(c(confint(b))), c(-0.7261239,-0.6921794), tolerance=1e-5) }) glmmTMB/tests/AAAtest-all.R0000644000176200001440000000052013614324717015073 0ustar liggesusersif(require("testthat")) { pkg <- "glmmTMB" require(pkg, character.only=TRUE) print(sessionInfo()) test_check(pkg, reporter="summary") print(warnings()) # TODO? catch most of these by expect_warning(..) } else { warnings("Package 'testthat' not available, cannot run unit tests for package", sQuote(pkg)) } glmmTMB/configure.ac0000644000176200001440000000433013614324717014047 0ustar liggesusers## https://github.com/USCBiostats/software-dev/wiki/Setting-up-optional-OpenMP-support ## see also: https://stackoverflow.com/questions/5298830/how-to-include-m4-files-in-autoconf ## https://github.com/wch/r-source/blob/568e8affd870537f4f8d862e61f131848a6a9a86/m4/openmp.m4 # -*- Autoconf -*- # glmmTMB configure.ac # (with some code borrowed from RcppArmadillo configure.ac # and ARTP2 configure.ac) # # Process this file with autoconf to produce a configure script. AC_PREREQ([2.69]) AC_INIT(glmmTMB, m4_esyscmd_s([awk '/^Version:/ {print $2}' DESCRIPTION])) ## Set R_HOME, respecting an environment variable if one is set : ${R_HOME=$(R RHOME)} if test -z "${R_HOME}"; then AC_MSG_ERROR([Could not determine R_HOME.]) fi ## Use R to set CXX and CXXFLAGS CXX=$(${R_HOME}/bin/R CMD config CXX) CXXFLAGS=$("${R_HOME}/bin/R" CMD config CXXFLAGS) ## We are using C++ AC_LANG(C++) AC_REQUIRE_CPP dnl this the meat of R's m4/openmp.m4 OPENMP_[]_AC_LANG_PREFIX[]FLAGS= AC_ARG_ENABLE([openmp], [AS_HELP_STRING([--disable-openmp], [do not use OpenMP])]) if test "$enable_openmp" != no; then AC_CACHE_CHECK([for $[]_AC_CC[] option to support OpenMP], [ac_cv_prog_[]_AC_LANG_ABBREV[]_openmp], [AC_LINK_IFELSE([_AC_LANG_OPENMP], [ac_cv_prog_[]_AC_LANG_ABBREV[]_openmp='none needed'], [ac_cv_prog_[]_AC_LANG_ABBREV[]_openmp='unsupported' for ac_option in -fopenmp -xopenmp -qopenmp \ -openmp -mp -omp -qsmp=omp -homp \ -fopenmp=libomp \ -Popenmp --openmp; do ac_save_[]_AC_LANG_PREFIX[]FLAGS=$[]_AC_LANG_PREFIX[]FLAGS _AC_LANG_PREFIX[]FLAGS="$[]_AC_LANG_PREFIX[]FLAGS $ac_option" AC_LINK_IFELSE([_AC_LANG_OPENMP], [ac_cv_prog_[]_AC_LANG_ABBREV[]_openmp=$ac_option]) _AC_LANG_PREFIX[]FLAGS=$ac_save_[]_AC_LANG_PREFIX[]FLAGS if test "$ac_cv_prog_[]_AC_LANG_ABBREV[]_openmp" != unsupported; then break fi done])]) case $ac_cv_prog_[]_AC_LANG_ABBREV[]_openmp in #( "none needed" | unsupported) ;; #( *) OPENMP_[]_AC_LANG_PREFIX[]FLAGS=$ac_cv_prog_[]_AC_LANG_ABBREV[]_openmp ;; esac fi AC_SUBST(OPENMP_CXXFLAGS) AC_CONFIG_FILES([src/Makevars]) AC_OUTPUT glmmTMB/src/0000755000176200001440000000000013616062000012332 5ustar liggesusersglmmTMB/src/glmmTMB.cpp0000644000176200001440000007045613614324717014367 0ustar liggesusers#include #include "init.h" // don't need to include omp.h; we get it via TMB.hpp namespace glmmtmb{ template Type dbetabinom(Type y, Type a, Type b, Type n, int give_log=0) { /* Wikipedia: f(k|n,\alpha,\beta) = \frac{\Gamma(n+1)}{\Gamma(k+1)\Gamma(n-k+1)} \frac{\Gamma(k+\alpha)\Gamma(n-k+\beta)}{\Gamma(n+\alpha+\beta)} \frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)} */ Type logres = lgamma(n + 1) - lgamma(y + 1) - lgamma(n - y + 1) + lgamma(y + a) + lgamma(n - y + b) - lgamma(n + a + b) + lgamma(a + b) - lgamma(a) - lgamma(b) ; if(!give_log) return exp(logres); else return logres; } template Type dgenpois(Type y, Type theta, Type lambda, int give_log=0) { /* f(y|\theta,\lambda) = \frac{\theta(theta+\lambda y)^{y-1}e^{-\theta-\lambda y}}{y \!} */ Type logres = log(theta) + (y - 1) * log(theta + lambda * y) - theta - lambda * y - lgamma(y + Type(1)); if(!give_log) return exp(logres); else return logres; } /* Simulate from generalized poisson distribution */ template Type rgenpois(Type theta, Type lambda) { // Copied from R function HMMpa::rgenpois Type ans = Type(0); Type random_number = runif(Type(0), Type(1)); Type kum = dgenpois(Type(0), theta, lambda); while (random_number > kum) { ans = ans + Type(1); kum += dgenpois(ans, theta, lambda); } return ans; } /* Simulate from zero-truncated generalized poisson distribution */ template Type rtruncated_genpois(Type theta, Type lambda) { int nloop = 10000; int counter = 0; Type ans = rgenpois(theta, lambda); while(ans < Type(1) && counter < nloop) { ans = rgenpois(theta, lambda); counter++; } if(ans < 1.) warning("Zeros in simulation of zero-truncated data. Possibly due to low estimated mean."); return ans; } template bool isNA(Type x){ return R_IsNA(asDouble(x)); } extern "C" { /* See 'R-API: entry points to C-code' (Writing R-extensions) */ double Rf_logspace_sub (double logx, double logy); void Rf_pnorm_both(double x, double *cum, double *ccum, int i_tail, int log_p); } /* y(x) = logit_invcloglog(x) := log( exp(exp(x)) - 1 ) = logspace_sub( exp(x), 0 ) y'(x) = exp(x) + exp(x-y) = exp( logspace_add(x, x-y) ) */ TMB_ATOMIC_VECTOR_FUNCTION( // ATOMIC_NAME logit_invcloglog , // OUTPUT_DIM 1, // ATOMIC_DOUBLE ty[0] = Rf_logspace_sub(exp(tx[0]), 0.); , // ATOMIC_REVERSE px[0] = exp( logspace_add(tx[0], tx[0]-ty[0]) ) * py[0]; ) template Type logit_invcloglog(Type x) { CppAD::vector tx(1); tx[0] = x; return logit_invcloglog(tx)[0]; } /* y(x) = logit_pnorm(x) := logit( pnorm(x) ) = pnorm(x, lower.tail=TRUE, log.p=TRUE) - pnorm(x, lower.tail=FALSE, log.p=TRUE) y'(x) = dnorm(x) * ( (1+exp(y)) + (1+exp(-y)) ) */ double logit_pnorm(double x) { double log_p_lower, log_p_upper; Rf_pnorm_both(x, &log_p_lower, &log_p_upper, 2 /* both tails */, 1 /* log_p */); return log_p_lower - log_p_upper; } TMB_ATOMIC_VECTOR_FUNCTION( // ATOMIC_NAME logit_pnorm , // OUTPUT_DIM 1, // ATOMIC_DOUBLE ty[0] = logit_pnorm(tx[0]) , // ATOMIC_REVERSE Type zero = 0; Type tmp1 = logspace_add(zero, ty[0]); Type tmp2 = logspace_add(zero, -ty[0]); Type tmp3 = logspace_add(tmp1, tmp2); Type tmp4 = dnorm(tx[0], Type(0), Type(1), true) + tmp3; px[0] = exp( tmp4 ) * py[0]; ) template Type logit_pnorm(Type x) { CppAD::vector tx(1); tx[0] = x; return logit_pnorm(tx)[0]; } /* Calculate variance in compois family using V(X) = (logZ)''(loglambda) */ double compois_calc_var(double mean, double nu){ using atomic::compois_utils::calc_loglambda; using atomic::compois_utils::calc_logZ; double loglambda = calc_loglambda(log(mean), nu); typedef atomic::tiny_ad::variable<2, 1, double> ADdouble; ADdouble loglambda_ (loglambda, 0); ADdouble ans = calc_logZ(loglambda_, nu); return ans.getDeriv()[0]; } /* Simulate from zero-truncated Conway-Maxwell-Poisson distribution */ template Type rtruncated_compois2(Type mean, Type nu) { int nloop = 10000; int counter = 0; Type ans = rcompois2(mean, nu); while(ans < 1. && counter < nloop) { ans = rcompois2(mean, nu); counter++; } if(ans < 1.) warning("Zeros in simulation of zero-truncated data. Possibly due to low estimated mean."); return ans; } /* Simulate from tweedie distribution */ template Type rtweedie(Type mu, Type phi, Type p) { // Copied from R function tweedie::rtweedie Type lambda = pow(mu, 2. - p) / (phi * (2. - p)); Type alpha = (2. - p) / (1. - p); Type gam = phi * (p - 1.) * pow(mu, p - 1.); int N = (int) asDouble(rpois(lambda)); Type ans = rgamma(N, -alpha /* shape */, gam /* scale */).sum(); return ans; } } /* Interface to compois variance */ extern "C" { SEXP compois_calc_var(SEXP mean, SEXP nu) { if (LENGTH(mean) != LENGTH(nu)) error("'mean' and 'nu' must be vectors of same length."); SEXP ans = PROTECT(allocVector(REALSXP, LENGTH(mean))); for(int i=0; i Type inverse_linkfun(Type eta, int link) { Type ans; switch (link) { case log_link: ans = exp(eta); break; case identity_link: ans = eta; break; case logit_link: ans = invlogit(eta); break; case probit_link: ans = pnorm(eta); break; case cloglog_link: ans = Type(1) - exp(-exp(eta)); break; case inverse_link: ans = Type(1) / eta; break; case sqrt_link: ans = eta*eta; // pow(eta, Type(2)) doesn't work ... ? break; // TODO: Implement remaining links default: error("Link not implemented!"); } // End switch return ans; } /* logit transformed inverse_linkfun without losing too much accuracy */ template Type logit_inverse_linkfun(Type eta, int link) { Type ans; switch (link) { case logit_link: ans = eta; break; case probit_link: ans = glmmtmb::logit_pnorm(eta); break; case cloglog_link: ans = glmmtmb::logit_invcloglog(eta); break; default: ans = logit( inverse_linkfun(eta, link) ); } // End switch return ans; } /* log transformed inverse_linkfun without losing too much accuracy */ template Type log_inverse_linkfun(Type eta, int link) { Type ans; switch (link) { case log_link: ans = eta; break; default: ans = log( inverse_linkfun(eta, link) ); } // End switch return ans; } template struct per_term_info { // Input from R int blockCode; // Code that defines structure int blockSize; // Size of one block int blockReps; // Repeat block number of times int blockNumTheta; // Parameter count per block matrix dist; vector times;// For ar1 case // Report output matrix corr; vector sd; }; template struct terms_t : vector > { terms_t(SEXP x){ (*this).resize(LENGTH(x)); for(int i=0; i(t); } // Optionally, pass distance matrix: SEXP d = getListElement(y, "dist"); if(!isNull(d)){ RObjectTestExpectedType(d, &isMatrix, "dist"); (*this)(i).dist = asMatrix(d); } } } }; template Type termwise_nll(array &U, vector theta, per_term_info& term, bool do_simulate = false) { Type ans = 0; if (term.blockCode == diag_covstruct){ // case: diag_covstruct vector sd = exp(theta); for(int i = 0; i < term.blockReps; i++){ ans -= dnorm(vector(U.col(i)), Type(0), sd, true).sum(); if (do_simulate) { U.col(i) = rnorm(Type(0), sd); } } term.sd = sd; // For report } else if (term.blockCode == us_covstruct){ // case: us_covstruct int n = term.blockSize; vector logsd = theta.head(n); vector corr_transf = theta.tail(theta.size() - n); vector sd = exp(logsd); density::UNSTRUCTURED_CORR_t nldens(corr_transf); density::VECSCALE_t > scnldens = density::VECSCALE(nldens, sd); for(int i = 0; i < term.blockReps; i++){ ans += scnldens(U.col(i)); if (do_simulate) { U.col(i) = sd * nldens.simulate(); } } term.corr = nldens.cov(); // For report term.sd = sd; // For report } else if (term.blockCode == cs_covstruct){ // case: cs_covstruct int n = term.blockSize; vector logsd = theta.head(n); Type corr_transf = theta(n); vector sd = exp(logsd); Type a = Type(1) / (Type(n) - Type(1)); Type rho = invlogit(corr_transf) * (Type(1) + a) - a; matrix corr(n,n); for(int i=0; i nldens(corr); density::VECSCALE_t > scnldens = density::VECSCALE(nldens, sd); for(int i = 0; i < term.blockReps; i++){ ans += scnldens(U.col(i)); if (do_simulate) { U.col(i) = sd * nldens.simulate(); } } term.corr = nldens.cov(); // For report term.sd = sd; // For report } else if (term.blockCode == toep_covstruct){ // case: toep_covstruct int n = term.blockSize; vector logsd = theta.head(n); vector sd = exp(logsd); vector parms = theta.tail(n-1); // Corr parms parms = parms / sqrt(Type(1.0) + parms * parms ); // Now in (-1,1) matrix corr(n,n); for(int i=0; i j ? i-j : j-i) - 1 ) ); density::MVNORM_t nldens(corr); density::VECSCALE_t > scnldens = density::VECSCALE(nldens, sd); for(int i = 0; i < term.blockReps; i++){ ans += scnldens(U.col(i)); if (do_simulate) { U.col(i) = sd * nldens.simulate(); } } term.corr = nldens.cov(); // For report term.sd = sd; // For report } else if (term.blockCode == ar1_covstruct){ // case: ar1_covstruct // * NOTE: Valid parameter space is phi in [-1, 1] // * NOTE: 'times' not used as we assume unit distance between consecutive time points. int n = term.blockSize; Type logsd = theta(0); Type corr_transf = theta(1); Type phi = corr_transf / sqrt(1.0 + pow(corr_transf, 2)); Type sd = exp(logsd); for(int j = 0; j < term.blockReps; j++){ ans -= dnorm(U(0, j), Type(0), sd, true); // Initialize if (do_simulate) { U(0, j) = rnorm(Type(0), sd); } for(int i=1; i::value) { // Disable AD for this part term.corr.resize(n,n); term.sd.resize(n); for(int i=0; i::value) { // Disable AD for this part term.corr.resize(n,n); term.sd.resize(n); for(int i=0; i dist = term.dist; if(! ( dist.cols() == n && dist.rows() == n ) ) error ("Dimension of distance matrix must equal blocksize."); // First parameter is sd Type sd = exp( theta(0) ); // Setup correlation matrix matrix corr(n,n); for(int i=0; i nldens(corr); density::SCALE_t > scnldens = density::SCALE(nldens, sd); for(int i = 0; i < term.blockReps; i++){ ans += scnldens(U.col(i)); if (do_simulate) { U.col(i) = sd * nldens.simulate(); } } term.corr = corr; // For report term.sd.resize(n); // For report term.sd.fill(sd); } else error("covStruct not implemented!"); return ans; } template Type allterms_nll(vector &u, vector theta, vector >& terms, bool do_simulate = false) { Type ans = 0; int upointer = 0; int tpointer = 0; int nr, np = 0, offset; for(int i=0; i < terms.size(); i++){ nr = terms(i).blockSize * terms(i).blockReps; // Note: 'blockNumTheta=0' ==> Same parameters as previous term. bool emptyTheta = ( terms(i).blockNumTheta == 0 ); offset = ( emptyTheta ? -np : 0 ); np = ( emptyTheta ? np : terms(i).blockNumTheta ); vector dim(2); dim << terms(i).blockSize, terms(i).blockReps; array useg( &u(upointer), dim); vector tseg = theta.segment(tpointer + offset, np); ans += termwise_nll(useg, tseg, terms(i), do_simulate); upointer += nr; tpointer += terms(i).blockNumTheta; } return ans; } template Type objective_function::operator() () { // DELETE when we're sure this is redundant ... // #ifdef _OPENMP // Set max number of OpenMP threads to help us optimize faster // max_parallel_regions = omp_get_max_threads(); // #endif DATA_MATRIX(X); DATA_SPARSE_MATRIX(Z); DATA_MATRIX(Xzi); DATA_SPARSE_MATRIX(Zzi); DATA_MATRIX(Xd); DATA_VECTOR(yobs); DATA_VECTOR(size); //only used in binomial DATA_VECTOR(weights); DATA_VECTOR(offset); DATA_VECTOR(zioffset); DATA_VECTOR(doffset); // Define covariance structure for the conditional model DATA_STRUCT(terms, terms_t); // Define covariance structure for the zero inflation DATA_STRUCT(termszi, terms_t); // Parameters related to design matrices PARAMETER_VECTOR(beta); PARAMETER_VECTOR(betazi); PARAMETER_VECTOR(b); PARAMETER_VECTOR(bzi); PARAMETER_VECTOR(betad); // Joint vector of covariance parameters PARAMETER_VECTOR(theta); PARAMETER_VECTOR(thetazi); // Extra family specific parameters (e.g. tweedie) PARAMETER_VECTOR(thetaf); DATA_INTEGER(family); DATA_INTEGER(link); // Flags DATA_INTEGER(ziPredictCode); bool zi_flag = (betazi.size() > 0); DATA_INTEGER(doPredict); DATA_IVECTOR(whichPredict); // One-Step-Ahead (OSA) residuals DATA_VECTOR_INDICATOR(keep, yobs); // Joint negative log-likelihood parallel_accumulator jnll(this); // Random effects jnll += allterms_nll(b, theta, terms, this->do_simulate); jnll += allterms_nll(bzi, thetazi, termszi, this->do_simulate); // Linear predictor vector eta = X * beta + Z * b + offset; vector etazi = Xzi * betazi + Zzi * bzi + zioffset; vector etad = Xd * betad + doffset; // Apply link vector mu(eta.size()); for (int i = 0; i < mu.size(); i++) mu(i) = inverse_linkfun(eta(i), link); vector pz = invlogit(etazi); vector phi = exp(etad); // "zero-truncated" likelihood: ignore zeros in positive distributions // exact zero: use for positive distributions (Gamma, beta) #define zt_lik_zero(x,loglik_exp) (zi_flag && (x == Type(0)) ? -INFINITY : loglik_exp) // close to zero: use for count data (cf binomial()$initialize) #define zt_lik_nearzero(x,loglik_exp) (zi_flag && (x < Type(0.001)) ? -INFINITY : loglik_exp) // Observation likelihood Type s1, s2, s3, log_nzprob; Type tmp_loglik; for (int i=0; i < yobs.size(); i++){ if ( !glmmtmb::isNA(yobs(i)) ) { switch (family) { case gaussian_family: tmp_loglik = dnorm(yobs(i), mu(i), sqrt(phi(i)), true); SIMULATE{yobs(i) = rnorm(mu(i), sqrt(phi(i)));} break; case poisson_family: tmp_loglik = dpois(yobs(i), mu(i), true); SIMULATE{yobs(i) = rpois(mu(i));} break; case binomial_family: s1 = logit_inverse_linkfun(eta(i), link); // logit(p) tmp_loglik = dbinom_robust(yobs(i), size(i), s1, true); SIMULATE{yobs(i) = rbinom(size(i), mu(i));} break; case Gamma_family: s1 = phi(i); // shape s2 = mu(i) / phi(i); // scale tmp_loglik = zt_lik_zero(yobs(i),dgamma(yobs(i), s1, s2, true)); SIMULATE{yobs(i) = rgamma(s1, s2);} break; case beta_family: // parameterization after Ferrari and Cribari-Neto 2004, betareg package s1 = mu(i)*phi(i); s2 = (Type(1)-mu(i))*phi(i); tmp_loglik = zt_lik_zero(yobs(i),dbeta(yobs(i), s1, s2, true)); SIMULATE{yobs(i) = rbeta(s1, s2);} break; case betabinomial_family: s1 = mu(i)*phi(i); // s1 = mu(i) * mu(i) / phi(i); s2 = (Type(1)-mu(i))*phi(i); // phi(i) / mu(i); tmp_loglik = glmmtmb::dbetabinom(yobs(i), s1, s2, size(i), true); SIMULATE { yobs(i) = rbinom(size(i), rbeta(s1, s2) ); } break; case nbinom1_family: case truncated_nbinom1_family: // Was: // s1 = mu(i); // s2 = mu(i) * (Type(1)+phi(i)); // (1+phi) guarantees that var >= mu // tmp_loglik = dnbinom2(yobs(i), s1, s2, true); s1 = log_inverse_linkfun(eta(i), link); // log(mu) s2 = s1 + etad(i) ; // log(var - mu) tmp_loglik = dnbinom_robust(yobs(i), s1, s2, true); SIMULATE { s1 = mu(i); s2 = mu(i) * (Type(1)+phi(i)); // (1+phi) guarantees that var >= mu yobs(i) = rnbinom2(s1, s2); } if( family == truncated_nbinom1_family ) { // s3 := log( 1. + phi(i) ) s3 = logspace_add( Type(0), etad(i) ); log_nzprob = logspace_sub( Type(0), -mu(i) / phi(i) * s3 ); // 1-prob(0) tmp_loglik -= log_nzprob; tmp_loglik = zt_lik_nearzero(yobs(i), tmp_loglik); SIMULATE{ s1 = mu(i)/phi(i);//sz s2 = 1/(1+phi(i)); //pb yobs(i) = Rf_qnbinom(asDouble(runif(dnbinom(Type(0), s1, s2), Type(1))), asDouble(s1), asDouble(s2), 1, 0); } } break; case nbinom2_family: case truncated_nbinom2_family: // Was: // s1 = mu(i); // s2 = mu(i) * (Type(1) + mu(i) / phi(i)); // tmp_loglik = dnbinom2(yobs(i), s1, s2, true); s1 = log_inverse_linkfun(eta(i), link); // log(mu) s2 = 2. * s1 - etad(i) ; // log(var - mu) tmp_loglik = dnbinom_robust(yobs(i), s1, s2, true); SIMULATE { s1 = mu(i); s2 = mu(i) * (Type(1) + mu(i) / phi(i)); yobs(i) = rnbinom2(s1, s2); } if (family == truncated_nbinom2_family) { // s3 := log( 1. + mu(i) / phi(i) ) s3 = logspace_add( Type(0), s1 - etad(i) ); log_nzprob = logspace_sub( Type(0), -phi(i) * s3 ); tmp_loglik -= log_nzprob; tmp_loglik = zt_lik_nearzero( yobs(i), tmp_loglik); SIMULATE{ s1 = phi(i); //sz s2 = phi(i)/(phi(i)+mu(i)); //pb yobs(i) = Rf_qnbinom(asDouble(runif(dnbinom(Type(0), s1, s2), Type(1))), asDouble(s1), asDouble(s2), 1, 0); } } break; case truncated_poisson_family: // Was: // if (mu(i)<1e-6) { // nzprob = mu(i)*(1-mu(i)/2); // } else { // nzprob = 1-exp(-mu(i)); // } // log(nzprob) = log( 1 - exp(-mu(i)) ) log_nzprob = logspace_sub(Type(0), -mu(i)); tmp_loglik = dpois(yobs(i), mu(i), true) - log_nzprob; tmp_loglik = zt_lik_nearzero(yobs(i), tmp_loglik); SIMULATE{ yobs(i) = Rf_qpois(asDouble(runif(dpois(Type(0), mu(i)), Type(1))), asDouble(mu(i)), 1, 0); } break; case genpois_family: s1 = mu(i) / sqrt(phi(i)); //theta s2 = Type(1) - Type(1)/sqrt(phi(i)); //lambda tmp_loglik = glmmtmb::dgenpois(yobs(i), s1, s2, true); SIMULATE{yobs(i)=glmmtmb::rgenpois(mu(i) / sqrt(phi(i)), Type(1) - Type(1)/sqrt(phi(i)));} break; case truncated_genpois_family: s1 = mu(i) / sqrt(phi(i)); //theta s2 = Type(1) - Type(1)/sqrt(phi(i)); //lambda log_nzprob = logspace_sub(Type(0), -s1); tmp_loglik = zt_lik_nearzero(yobs(i), glmmtmb::dgenpois(yobs(i), s1, s2, true) - log_nzprob); SIMULATE{yobs(i)=glmmtmb::rtruncated_genpois(mu(i) / sqrt(phi(i)), Type(1) - Type(1)/sqrt(phi(i)));} break; case compois_family: s1 = mu(i); //mean s2 = 1/phi(i); //nu tmp_loglik = dcompois2(yobs(i), s1, s2, true); SIMULATE{yobs(i)=rcompois2(mu(i), 1/phi(i));} break; case truncated_compois_family: s1 = mu(i); //mean s2 = 1/phi(i); //nu log_nzprob = logspace_sub(Type(0), dcompois2(Type(0), s1, s2, true)); tmp_loglik = zt_lik_nearzero(yobs(i), dcompois2(yobs(i), s1, s2, true) - log_nzprob); SIMULATE{yobs(i)=glmmtmb::rtruncated_compois2(mu(i), 1/phi(i));} break; case tweedie_family: s1 = mu(i); // mean s2 = phi(i); // phi s3 = invlogit(thetaf(0)) + Type(1); // p, 1 > corr(terms.size()); vector > sd(terms.size()); for(int i=0; i 0){ corr(i) = terms(i).corr; sd(i) = terms(i).sd; } } vector > corrzi(termszi.size()); vector > sdzi(termszi.size()); for(int i=0; i 0){ corrzi(i) = termszi(i).corr; sdzi(i) = termszi(i).sd; } } REPORT(corr); REPORT(sd); REPORT(corrzi); REPORT(sdzi); SIMULATE { REPORT(yobs); REPORT(b); REPORT(bzi); } // For predict if(ziPredictCode==disp_zipredictcode){ //zi irrelevant; just reusing variable switch(family){ case gaussian_family: mu = sqrt(phi); break; case Gamma_family: mu = 1/sqrt(phi); break; default: mu = phi; } } else { if(zi_flag) { switch(ziPredictCode){ case corrected_zipredictcode: mu *= (Type(1) - pz); // Account for zi in prediction break; case uncorrected_zipredictcode: //mu = mu; // Predict mean of 'family' //comented out for clang 7.0.0. with no effect break; case prob_zipredictcode: mu = pz; // Predict zi probability break; default: error("Invalid 'ziPredictCode'"); } }} whichPredict -= 1; // R-index -> C-index vector mu_predict = mu(whichPredict); REPORT(mu_predict); // ADREPORT expensive for long vectors - only needed by predict() // method. if (doPredict) ADREPORT(mu_predict); return jnll; } glmmTMB/src/init.h0000644000176200001440000000241713614324717013470 0ustar liggesusers#include #include /* FIXME: Won't be needed in upcoming TMB versions */ #ifndef TMB_CALLDEFS #define TMB_CALLDEFS \ {"MakeADFunObject", (DL_FUNC) &MakeADFunObject, 4}, \ {"InfoADFunObject", (DL_FUNC) &InfoADFunObject, 1}, \ {"EvalADFunObject", (DL_FUNC) &EvalADFunObject, 3}, \ {"MakeDoubleFunObject", (DL_FUNC) &MakeDoubleFunObject, 3}, \ {"EvalDoubleFunObject", (DL_FUNC) &EvalDoubleFunObject, 3}, \ {"getParameterOrder", (DL_FUNC) &getParameterOrder, 3}, \ {"MakeADGradObject", (DL_FUNC) &MakeADGradObject, 3}, \ {"MakeADHessObject2", (DL_FUNC) &MakeADHessObject2, 4}, \ {"usingAtomics", (DL_FUNC) &usingAtomics, 0}, \ {"TMBconfig", (DL_FUNC) &TMBconfig, 2} #endif #define CALLDEF(name, n) {#name, (DL_FUNC) &name, n} extern "C" { SEXP compois_calc_var(SEXP mean, SEXP nu); const static R_CallMethodDef R_CallDef[] = { TMB_CALLDEFS, CALLDEF(compois_calc_var, 2), {NULL, NULL, 0} }; void R_init_glmmTMB(DllInfo *dll) { R_registerRoutines(dll, NULL, R_CallDef, NULL, NULL); R_useDynamicSymbols(dll, FALSE); #ifdef TMB_CCALLABLES TMB_CCALLABLES("glmmTMB"); #endif } } glmmTMB/src/Makevars.in0000644000176200001440000000043413614324717014452 0ustar liggesusersPKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) @OPENMP_CXXFLAGS@ # 1.2.4 Using C++11 code CXX_STD = CXX11 # Besides of the -fopenmp flag, here I'm telling armadillo to use # 64BIT_WORD which removes the matrix size limit constraint. PKG_CXXFLAGS=@OPENMP_CXXFLAGS@ -DARMA_64BIT_WORD glmmTMB/vignettes/0000755000176200001440000000000013616062000013553 5ustar liggesusersglmmTMB/vignettes/InstEvalTimings.rda0000644000176200001440000000064013614324717017341 0ustar liggesusers r0b```b`f@$X84OInbg^qkYb0P EwY'ec0 ?``qcgi n* UGeN0q%rRRs,,D=='77 eM-BȚX 7--1$,D>oAlS|;ͽ`pb.ٓUzNt!?%JfCr]}BL<36(`pP4A/Eb ZC ByL _hppgj_%>5o'f488Y@gQ~^^bn*k`j"p2A+Mhf^\)%ziE 0KglmmTMB/vignettes/parallel.Rmd0000644000176200001440000000704413614324717016036 0ustar liggesusers--- title: "Parallel optimization using glmmTMB" author: "Nafis Sadat" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{parallel optimization} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- A new, experimental feature of `glmmTMB` is the ability to parallelize the optimization process. This vignette shows an example and timing of a simple model fit with and without parallelizing across threads. If your OS supports OpenMP parallelization and R was installed using OpenMP, `glmmTMB` will automatically pick up the OpenMP flags from R's `Makevars` and compile the C++ model with OpenMP support. If the flag is not available, then the model will be compiled with serial optimization only. ```{r setup, include=FALSE, message=FALSE} library(knitr) ``` Load packages: ```{r libs,message=FALSE} library(glmmTMB) set.seed(1) nt <- min(parallel::detectCores(),5) ``` Simulate a dataset with large `N`: ```{r simulate1} N <- 3e5 xdata <- rnorm(N, 1, 2) ydata <- 0.3 + 0.4*xdata + rnorm(N, 0, 0.25) ``` First, we fit the model serially. We can pass the number of parallelizing process we want using the `parallel` parameter in `glmmTMBcontrol`: ```{r fit1} system.time( model1 <- glmmTMB(formula = ydata ~ 1 + xdata, control = glmmTMBControl(parallel = 1)) ) ``` Now, we fit the same model using five threads (or as many as possible - `r nt` in this case): ```{r fit2} system.time( model2 <- glmmTMB(formula = ydata ~ 1 + xdata, control = glmmTMBControl(parallel = nt)) ) ``` The speed-up is definitely more visible on models with a much larger number of observations, or in models with random effects. Here's an example where we have an IID Gaussian random effect. We first simulate the data with 200 groups (our random effect): ```{r simulate2} xdata <- rnorm(N, 1, 2) groups <- 200 data_use <- data.frame(obs = 1:N) data_use <- within(data_use, { group_var <- rep(seq(groups), times = nrow(data_use) / groups) group_intercept <- rnorm(groups, 0, 0.1)[group_var] xdata <- xdata ydata <- 0.3 + group_intercept + 0.5*xdata + rnorm(N, 0, 0.25) }) ``` We fit the random effect model, first with a single thread: ```{r fit3} (t_serial <- system.time( model3 <- glmmTMB(formula = ydata ~ 1 + xdata + (1 | group_var), data = data_use, control = glmmTMBControl(parallel = 1)) ) ) ``` Now we fit the same model, but using `r nt` threads. The speed-up is more noticeable with this model. ```{r fit4} (t_parallel <- system.time( update(model3, control = glmmTMBControl(parallel = nt)) ) ) ``` ## Notes on OpenMP support From [Writing R Extensions](https://cran.r-project.org/doc/manuals/r-devel/R-exts.html#OpenMP-support): > Apple builds of clang on macOS currently have no OpenMP support, but CRAN binary packages are built with a clang-based toolchain which supports OpenMP. http://www.openmp.org/resources/openmp-compilers-tools gives some idea of what compilers support what versions. > The performance of OpenMP varies substantially between platforms. The Windows implementation has substantial overheads, so is only beneficial if quite substantial tasks are run in parallel. Also, on Windows new threads are started with the default FPU control word, so computations done on OpenMP threads will not make use of extended-precision arithmetic which is the default for the main process. ## System information This report was built using `r nt` parallel threads (on a machine with a total of `r parallel::detectCores()` cores) ```{r SI} print(sessionInfo(), locale=FALSE) ``` glmmTMB/vignettes/covstruct.rmd0000644000176200001440000004053413614324717016337 0ustar liggesusers--- title: "Covariance structures with glmmTMB" author: "Kasper Kristensen" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{random effect structures} %\VignettePackage{glmmTMB} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} params: EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true") --- ```{r setup, include=FALSE, message=FALSE} library(knitr) library(glmmTMB) library(MASS) ## for mvrnorm() library(TMB) ## for tmbprofile() ## devtools::install_github("kaskr/adcomp/TMB") ## get development version knitr::opts_chunk$set(echo = TRUE, eval=if (isTRUE(exists("params"))) params$EVAL else FALSE) ## turned off caching for now: got error in chunk 'fit.us.2' ## Error in retape() : ## Error when reading the variable: 'thetaf'. Please check data and parameters. ## In addition: Warning message: ## In retape() : Expected object. Got NULL. set.seed(1) ## run this in interactive session if you actually want to evaluate chunks ... ## Sys.setenv(NOT_CRAN="true") ``` This vignette demonstrates some of the covariance structures available in the `glmmTMB` package. Currently the available covariance structures are: | Covariance | Notation | Parameter count | Requirement | |----------------------------------|---------------|-----------------|-------------| | Heterogeneous unstructured | `us` | $n(n+1)/2$ | | | Heterogeneous Toeplitz | `toep` | $2n-1$ | | | Heterogeneous compound symmetry | `cs` | $n+1$ | | | Heterogeneous diagonal | `diag` | $n$ | | | AR(1) | `ar1` | $2$ | | | Ornstein–Uhlenbeck | `ou` | $2$ | Coordinates | | Spatial exponential | `exp` | $2$ | Coordinates | | Spatial Gaussian | `gau` | $2$ | Coordinates | | Spatial Matern | `mat` | $3$ | Coordinates | The word 'heterogeneous' refers to the marginal variances of the model. Beyond correlation parameters, a heterogeneous structure uses $n$ additional variance parameters where $n$ is the dimension. Some of the structures require temporal or spatial coordinates. We will show examples of this in a later section. ## The AR(1) covariance structure ### Demonstration on simulated data First, let's consider a simple time series model. Assume that our measurements $Y(t)$ are given at discrete times $t \in \{1,...,n\}$ by $$Y(t) = \mu + X(t) + \varepsilon(t)$$ where - $\mu$ is the mean value parameter. - $X(t)$ is a stationary AR(1) process, i.e. has covariance $cov(X(s), X(t)) = \sigma^2\exp(-\theta |t-s|)$. - $\varepsilon(t)$ is iid. $N(0,\sigma_0^2)$ measurement error. A simulation experiment is set up using the parameters | Description | Parameter | Value | |------------------------|---------------|-------| | Mean | $\mu$ | 0 | | Process variance | $\sigma^2$ | 1 | | Measurement variance | $\sigma_0^2$ | 1 | | One-step correlation | $e^{-\theta}$ | 0.7 | The following R-code draws a simulation based on these parameter values. For illustration purposes we consider a very short time series. ```{r sim1, eval=TRUE} n <- 6 ## Number of time points x <- mvrnorm(mu = rep(0,n), Sigma = .7 ^ as.matrix(dist(1:n)) ) ## Simulate the process using the MASS package y <- x + rnorm(n) ## Add measurement noise ``` In order to fit the model with `glmmTMB` we must first specify a time variable as a *factor*. The factor *levels* correspond to unit spaced time points. ```{r simtimes} times <- factor(1:n) levels(times) ``` We also need a grouping variable. In the current case there is only one time-series so the grouping is: ```{r simgroup} group <- factor(rep(1,n)) ``` We combine the data into a single data frame (not absolutely required, but good practice): ```{r simcomb} dat0 <- data.frame(y,times,group) ``` Now fit the model using ```{r fitar1, eval=FALSE} glmmTMB(y ~ ar1(times + 0 | group), data=dat0) ``` This formula notation follows that of the `lme4` package. - The left hand side of the bar `times + 0` corresponds to a design matrix $Z$ linking observation vector $y$ (rows) with a random effects vector $u$ (columns). - The distribution of $u$ is `ar1` (this is the only `glmmTMB` specific part of the formula). - The right hand side of the bar splits the above specification independently among groups. Each group has its own separate $u$ vector but shares the same parameters for the covariance structure. After running the model, we find the parameter estimates $\mu$ (intercept), $\sigma_0^2$ (dispersion), $\sigma$ (Std. Dev.) and $e^{-\theta}$ (First off-diagonal of "Corr") in the output: > FIXME: Try a longer time series when the print.VarCorr is fixed. ```{r ar0fit,echo=FALSE} glmmTMB(y ~ ar1(times + 0 | group), data=dat0) ``` ### Increasing the sample size A single time series of 6 time points is not sufficient to identify the parameters. We could either increase the length of the time series or increase the number of groups. We'll try the latter: ```{r simGroup} simGroup <- function(g, n=6, rho=0.7) { x <- mvrnorm(mu = rep(0,n), Sigma = rho ^ as.matrix(dist(1:n)) ) ## Simulate the process y <- x + rnorm(n) ## Add measurement noise times <- factor(1:n) group <- factor(rep(g,n)) data.frame(y, times, group) } simGroup(1) ``` Generate a dataset with 1000 groups: ```{r simGroup2} dat1 <- do.call("rbind", lapply(1:1000, simGroup) ) ``` And fitting the model on this larger dataset gives estimates close to the true values (AR standard deviation=1, residual (measurement) standard deviation=1, autocorrelation=0.7): ```{r fit.ar1} (fit.ar1 <- glmmTMB(y ~ ar1(times + 0 | group), data=dat1)) ``` ## The unstructured covariance We can try to fit an unstructured covariance to the previous dataset `dat`. For this case an unstructured covariance has `r (n*n-n)/2` correlation parameters and `r n` variance parameters. Adding $\sigma_0^2 I$ on top would cause a strict overparameterization, as these would be redundant with the diagonal elements in the covariance matrix. Hence, when fitting the model with `glmmTMB`, we have to disable the $\varepsilon$ term (the dispersion) by setting `dispformula=~0`: ```{r fit.us} fit.us <- glmmTMB(y ~ us(times + 0 | group), data=dat1, dispformula=~0) fit.us$sdr$pdHess ## Converged ? ``` The estimated variance and correlation parameters are: ```{r fit.us.vc} VarCorr(fit.us) ``` \newcommand{\textsub}{2}{#1_{{\text \small #2}}} The estimated correlation is approximately constant along diagonals (apparent Toeplitz structure) and we note that the first off-diagonal is now ca. half the true value (0.7) because the dispersion is effectively included in the estimated covariance matrix (i.e. $\rho' = \rho \textsub{\sigma^2}{AR}/(\textsub{\sigma^2}{AR} + \textsub{sigma^2}{meas})$). ## The Toeplitz structure The next natural step would be to reduce the number of parameters by collecting correlation parameters within the same off-diagonal. This amounts to `r (n-1)` correlation parameters and `r n` variance parameters. > FIXME: Explain why dispformula=~1 causes over-parameterization ```{r fit.toep} fit.toep <- glmmTMB(y ~ toep(times + 0 | group), data=dat1, dispformula=~0) fit.toep$sdr$pdHess ## Converged ? ``` The estimated variance and correlation parameters are: ```{r fit.toep.vc} (vc.toep <- VarCorr(fit.toep)) ``` The diagonal elements are all approximately equal to the true total variance ($\textsub{\sigma^2}{AR} + \textsub{sigma^2}{meas}$=2), and the off-diagonal elements are approximately equal to the expected value of 0.7/2=0.35. ```{r fit.toep.vc.diag} vc1 <- vc.toep$cond[[1]] ## first term of var-cov for RE of conditional model summary(diag(vc1)) summary(vc1[row(vc1)!=col(vc1)]) ``` We can get a *slightly* better estimate of the variance by using REML estimation (however, the estimate of the correlations seems to have gotten slightly worse): ```{r fit.toep.reml} fit.toep.reml <- update(fit.toep, REML=TRUE) vc1R <- VarCorr(fit.toep.reml)$cond[[1]] summary(diag(vc1R)) summary(vc1R[row(vc1R)!=col(vc1R)]) ``` ## Compound symmetry The compound symmetry structure collects all off-diagonal elements of the correlation matrix to one common value. > FIXME: Explain why dispformula=~1 causes over-parameterization ```{r fit.cs} fit.cs <- glmmTMB(y ~ cs(times + 0 | group), data=dat1, dispformula=~0) fit.cs$sdr$pdHess ## Converged ? ``` The estimated variance and correlation parameters are: ```{r fit.cs.vc} VarCorr(fit.cs) ``` ## Anova tables The models `ar1`, `toep`, and `us` are nested so we can use: ```{r anova1} anova(fit.ar1, fit.toep, fit.us) ``` `ar1` has the lowest AIC (it's the simplest model, and fits the data adequately); we can't reject the (true in this case!) null model that an AR1 structure is adequate to describe the data. The model `cs` is a sub-model of `toep`: ```{r anova2} anova(fit.cs, fit.toep) ``` Here we *can* reject the null hypothesis of compound symmetry (i.e., that all the pairwise correlations are the same). ## Adding coordinate information Coordinate information can be added to a variable using the `glmmTMB` function `numFactor`. This is necessary in order to use those covariance structures that require coordinates. For example, if we have the numeric coordinates ```{r sample2} x <- sample(1:2, 10, replace=TRUE) y <- sample(1:2, 10, replace=TRUE) ``` we can generate a factor representing $(x,y)$ coordinates by ```{r numFactor} (pos <- numFactor(x,y)) ``` Numeric coordinates can be recovered from the factor levels: ```{r parseNumLevels} parseNumLevels(levels(pos)) ``` In order to try the remaining structures on our test data we re-interpret the time factor using `numFactor`: ```{r numFactor2} dat1$times <- numFactor(dat1$times) levels(dat1$times) ``` ## Ornstein–Uhlenbeck Having the numeric times encoded in the factor levels we can now try the Ornstein–Uhlenbeck covariance structure. ```{r fit.ou} fit.ou <- glmmTMB(y ~ ou(times + 0 | group), data=dat1) fit.ou$sdr$pdHess ## Converged ? ``` It should give the exact same results as `ar1` in this case since the times are equidistant: ```{r fit.ou.vc} VarCorr(fit.ou) ``` However, note the differences between `ou` and `ar1`: - `ou` can handle irregular time points. - `ou` only allows positive correlation between neighboring time points. ## Spatial correlations The structures `exp`, `gau` and `mat` are meant to used for spatial data. They all require a Euclidean distance matrix which is calculated internally based on the coordinates. Here, we will try these models on the simulated time series data. An example with spatial data is presented in a later section. ### Matern ```{r fit.mat} fit.mat <- glmmTMB(y ~ mat(times + 0 | group), data=dat1, dispformula=~0) fit.mat$sdr$pdHess ## Converged ? ``` ```{r fit.mat.vc} VarCorr(fit.mat) ``` ### Gaussian "Gaussian" refers here to a Gaussian decay in correlation with distance, i.e. $\rho = \exp(-d x^2)$, not to the conditional distribution ("family"). ```{r fit.gau} fit.gau <- glmmTMB(y ~ gau(times + 0 | group), data=dat1, dispformula=~0) fit.gau$sdr$pdHess ## Converged ? ``` ```{r fit.gau.vc} VarCorr(fit.gau) ``` ### Exponential ```{r fit.exp} fit.exp <- glmmTMB(y ~ exp(times + 0 | group), data=dat1) fit.exp$sdr$pdHess ## Converged ? ``` ```{r fit.exp.vc} VarCorr(fit.exp) ``` ### A spatial covariance example Starting out with the built in `volcano` dataset we reshape it to a `data.frame` with pixel intensity `z` and pixel position `x` and `y`: ```{r spatial_data} d <- data.frame(z = as.vector(volcano), x = as.vector(row(volcano)), y = as.vector(col(volcano))) ``` Next, add random normal noise to the pixel intensities and extract a small subset of 100 pixels. This is our spatial dataset: ```{r spatial_sub_sample} set.seed(1) d$z <- d$z + rnorm(length(volcano), sd=15) d <- d[sample(nrow(d), 100), ] ``` Display sampled noisy volcano data: ```{r volcano_data_image} volcano.data <- array(NA, dim(volcano)) volcano.data[cbind(d$x, d$y)] <- d$z image(volcano.data, main="Spatial data") ``` Based on this data, we'll attempt to re-construct the original image. As model, it is assumed that the original image `image(volcano)` is a realization of a random field with correlation decaying exponentially with distance between pixels. Denoting by $u(x,y)$ this random field the model for the observations is \[ z_{i} = \mu + u(x_i,y_i) + \varepsilon_i \] To fit the model, a `numFactor` and a dummy grouping variable must be added to the dataset: ```{r spatial_add_pos_and_group} d$pos <- numFactor(d$x, d$y) d$group <- factor(rep(1, nrow(d))) ``` The model is fit by ```{r fit_spatial_model, cache=TRUE} f <- glmmTMB(z ~ 1 + exp(pos + 0 | group), data=d) ``` Recall that a standard deviation `sd=15` was used to distort the image. A confidence interval for this parameter is ```{r confint_sigma} confint(f, "sigma") ``` The glmmTMB `predict` method can predict unseen levels of the random effects. For instance to predict a 3-by-3 corner of the image one could construct the new data: ```{r newdata_corner} newdata <- data.frame( pos=numFactor(expand.grid(x=1:3,y=1:3)) ) newdata$group <- factor(rep(1, nrow(newdata))) newdata ``` and predict using ```{r predict_corner} predict(f, newdata, type="response", allow.new.levels=TRUE) ``` A specific image column can thus be predicted using the function ```{r predict_column} predict_col <- function(i) { newdata <- data.frame( pos = numFactor(expand.grid(1:87,i))) newdata$group <- factor(rep(1,nrow(newdata))) predict(f, newdata=newdata, type="response", allow.new.levels=TRUE) } ``` Prediction of the entire image is carried out by (this takes a while...): ```{r predict_all} pred <- sapply(1:61, predict_col) ``` Finally plot the re-constructed image by ```{r image_results} image(pred, main="Reconstruction") ``` ## Mappings For various advanced purposes, such as computing likelihood profiles, it is useful to know the details of the parameterization of the models - the scale on which the parameters are defined (e.g. standard deviation, variance, or log-standard deviation for variance parameters) and their order. ### Unstructured For an unstructured matrix of size `n`, parameters `1:n` represent the log-standard deviations while the remaining `n(n-1)/2` (i.e. `(n+1):(n:(n*(n+1)/2))`) are the elements of the *scaled* Cholesky factor of the correlation matrix, filled in row-wise order (see [TMB documentation](http://kaskr.github.io/adcomp/classUNSTRUCTURED__CORR__t.html)). In particular, if $L$ is the lower-triangular matrix with 1 on the diagonal and the correlation parameters in the lower triangle, then the correlation matrix is defined as $\Sigma = D^{-1/2} L L^\top D^{-1/2}$, where $D = \textrm{diag}(L L^\top)$. For a single correlation parameter $\theta_0$, this works out to $\rho = \theta_0/(1+\theta_0^2)$. ```{r fit.us.2} vv0 <- VarCorr(fit.us) vv1 <- vv0$cond$group ## extract 'naked' V-C matrix n <- nrow(vv1) rpars <- getME(fit.us,"theta") ## extract V-C parameters ## first n parameters are log-std devs: all.equal(unname(diag(vv1)),exp(rpars[1:n])^2) ## now try correlation parameters: cpars <- rpars[-(1:n)] length(cpars)==n*(n-1)/2 ## the expected number cc <- diag(n) cc[upper.tri(cc)] <- cpars L <- crossprod(cc) D <- diag(1/sqrt(diag(L))) D %*% L %*% D unname(attr(vv1,"correlation")) ``` > FIXME: why are these not quite the same? Not what I expected ```{r other_check} all.equal(c(cov2cor(vv1)),c(fit.us$obj$env$report(fit.us$fit$parfull)$corr[[1]])) ``` Profiling (experimental/exploratory): ```{r fit.us.profile,cache=TRUE} ## want $par, not $parfull: do NOT include conditional modes/'b' parameters ppar <- fit.us$fit$par length(ppar) range(which(names(ppar)=="theta")) ## the last n*(n+1)/2 parameters ## only 1 fixed effect parameter tt <- tmbprofile(fit.us$obj,2,trace=FALSE) ``` ```{r fit.us.profile.plot} plot(tt) confint(tt) ``` ```{r fit.cs.profile,cache=TRUE} ppar <- fit.cs$fit$par length(ppar) range(which(names(ppar)=="theta")) ## the last n*(n+1)/2 parameters ## only 1 fixed effect parameter, 1 dispersion parameter tt2 <- tmbprofile(fit.cs$obj,3,trace=FALSE) ``` ```{r fit.cs.profile.plot} plot(tt2) ``` glmmTMB/vignettes/troubleshooting.rmd0000644000176200001440000003216213614324717017530 0ustar liggesusers--- title: "Troubleshooting with glmmTMB" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{troubleshooting} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} params: EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true") --- ```{r load_lib,echo=FALSE} library(glmmTMB) knitr::opts_chunk$set(eval = if (isTRUE(exists("params"))) params$EVAL else FALSE) ``` This vignette covers common problems that occur while using `glmmTMB`. The contents will expand with experience. If your problem is not covered below, there's a chance it has been solved in the development version; try updating to the latest version of `glmmTMB` on GitHub. # Warnings ## Model convergence problem; non-positive-definite Hessian matrix; NA values for likelihood/AIC/etc. This warning (`Model convergence problem; non-positive-definite Hessian matrix`) states that at `glmmTMB`'s maximum-likelihood estimate, the curvature of the negative log-likelihood surface is inconsistent with `glmmTMB` really having found the best fit (minimum): instead, the surface is downward-curving, or flat, in some direction(s). It will usually be accompanied by `NA` values for the standard errors, log-likelihood, AIC, and BIC, and deviance. When you run `summary()` on the resulting model, you'll get the warning `In sqrt(diag(vcov)) : NaNs produced`. These problems are most likely: - when a model is overparameterized (i.e. the data does not contain enough information to estimate the parameters reliably) - when a random-effect variance is estimated to be zero, or random-effect terms are estimated to be perfectly correlated ("singular fit": often caused by having too few levels of the random-effect grouping variable) - when zero-inflation is estimated to be near zero (a strongly negative zero-inflation parameter) - when dispersion is estimated to be near zero - when *complete separation* occurs in a binomial model: some categories in the model contain proportions that are either all 0 or all 1 How do we diagnose the problem? ### Example 1. Consider this example: ```{r non-pos-def,cache=TRUE, warning=FALSE} zinbm0 = glmmTMB(count~spp + (1|site), zi=~spp, Salamanders, family=nbinom2) ``` First, see if any of the estimated coefficients are extreme. If you're using a non-identity link function (e.g. log, logit), then parameter values with $|\beta|>10$ are suspect (for a logit link, this implies probabilities very close to 0 or 1; for a log link, this implies mean counts that are close to 0 or extremely large). Inspecting the fixed-effect estimates for this model: ```{r fixef_zinbm0} fixef(zinbm0) ``` The zero-inflation intercept parameter is tiny ($\approx -17$): since the parameters are estimated on the logit scale, we back-transform with `plogis(-17)` to see the at the zero-inflation probability for the baseline level is about $4 \times 10^{-8}$)). Many of the other ZI parameters are very large, compensating for the intercept: the estimated zero-inflation probabilities for all species are ```{r f_zi2} ff <- fixef(zinbm0)$zi round(plogis(c(sppGP=unname(ff[1]),ff[-1]+ff[1])),3) ``` Since the baseline probability is already effectively zero, making the intercept parameter larger or smaller will have very little effect - the likelihood is flat, which leads to the non-positive-definite warning. Now that we suspect the problem is in the zero-inflation component, we can try to come up with ways of simplifying the model: for example, we could use a model that compared the first species ("GP") to the rest: ```{r salfit2,cache=TRUE} Salamanders <- transform(Salamanders, GP=as.numeric(spp=="GP")) zinbm0_A = update(zinbm0, ziformula=~GP) ``` This fits without a warning, although the GP zero-inflation parameter is still extreme: ```{r salfit2_coef,cache=TRUE} fixef(zinbm0_A)[["zi"]] ``` Another possibility would be to fit the variation among species in the zero-inflation parameter as a random effect, rather than a fixed effect: this is slightly more parsimonious. This again fits without an error, although both the average level of zero-inflation and the among-species variation are estimated as very small: ```{r salfit3,cache=TRUE} zinbm0_B = update(zinbm0, ziformula=~(1|spp)) fixef(zinbm0_B)[["zi"]] VarCorr(zinbm0_B) ``` The original analysis considered variation in zero-inflation by site status (mined or not mined) rather than by species - this simpler model only tries to estimate two parameters (mined + difference between mined and no-mining) rather than 7 (one per species) for the zero-inflation model. ```{r zinbm1,cache=TRUE} zinbm1 = glmmTMB(count~spp + (1|site), zi=~mined, Salamanders, family=nbinom2) fixef(zinbm1)[["zi"]] ``` This again fits without a warning, but we see that the zero-inflation is effectively zero in the unmined ("minedno") condition (`plogis(0.38-17.5)` is approximately $4 \times 10^{-8}$). We can estimate the confidence interval, but it takes some extra work: the default Wald standard errors and confidence intervals are useless in this case. ```{r zinbm1_confint,cache=TRUE} ## at present we need to specify the parameter by number; for ## extreme cases need to specify the parameter range ## (not sure why the upper bound needs to be so high ... ?) cc = confint(zinbm1,method="uniroot",parm=9, parm.range=c(-20,20)) print(cc) ``` The lower CI is not defined; the upper CI is -2.08, i.e. we can state that the zero-inflation probability is less than `plogis(-2.08)` = 0.11. More broadly, general inspection of the data (e.g., plotting the response against potential covariates) should help to diagnose overly complex models. ### Example 2. In some cases, scaling predictor variables may help. For example, in this example from @phisanti, the results of `glm` and `glmmTMB` applied to a scaled version of the data set agree, while `glmmTMB` applied to the raw data set gives a non-positive-definite Hessian warning. ```{r fatfiberglmm} ## data taken from gamlss.data:plasma, originally ## http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/plasma.html load(system.file("vignette_data","plasma.rda", package="glmmTMB")) m4.1 <- glm(calories ~ fat*fiber, family = Gamma(link = "log"), data = plasma) m4.2 <- glmmTMB(calories ~ fat*fiber, family = Gamma(link = "log"), data = plasma) ps <- transform(plasma,fat=scale(fat,center=FALSE),fiber=scale(fiber,center=FALSE)) m4.3 <- update(m4.2, data=ps) ## scaling factor for back-transforming standard deviations ss <- c(1, fatsc <- 1/attr(ps$fat,"scaled:scale"), fibsc <- 1/attr(ps$fiber,"scaled:scale"), fatsc*fibsc) ## combine SEs, suppressing the warning from the unscaled model s_vals <- cbind(glm=sqrt(diag(vcov(m4.1))), glmmTMB_unsc=suppressWarnings(sqrt(diag(vcov(m4.2)$cond))), glmmTMB_sc=sqrt(diag(vcov(m4.3)$cond))*ss) print(s_vals,digits=3) ``` ## Example 3. Here is another example (from Samantha Sherman): ```{r load_ss_ex} load(system.file("vignette_data","troubleshooting.rda",package="glmmTMB")) ``` The first model gives the specified warning when it runs, as well as the other symptoms such as `NA` values for the likelihood: ```{r ss_ex_mod1} summary(mod1) ``` We can immediately see that the dispersion is very small and that the zero-inflation parameter is strongly negative. However, we'll develop some fancier machinery that checks the variance-covariance matrix or Hessian of the model, finds eigenvalues that are negative or close to zero, and identifies which model components contribute to those eigenvalues: ```{r diagnose_vcov} diagnose_vcov <- function(model, tol=1e-5, digits=2, analyze_hessian=FALSE) { vv <- vcov(model, full=TRUE) nn <- rownames(vv) if (!all(is.finite(vv))) { if (missing(analyze_hessian)) warning("analyzing Hessian, not vcov") if (!analyze_hessian) stop("can't analyze vcov") analyze_hessian <- TRUE } if (analyze_hessian) { par.fixed <- model$obj$env$last.par.best r <- model$obj$env$random if (!is.null(r)) par.fixed <- par.fixed[-r] vv <- optimHess(par.fixed, fn=model$obj$fn, gr=model$obj$gr) ## note vv is now HESSIAN, not vcov } ee <- eigen(vv) if (all(ee$values>tol)) {message("var-cov matrix OK"); return(invisible(NULL))} ## find negative or small-positive eigenvalues (flat/wrong curvature) bad_evals <- which(ee$values Warning in nlminb(start = par, objective = fn, gradient = gr) : NA/NaN function evaluation This warning occurs when the optimizer visits a region of parameter space that is invalid. It is not a problem as long as the optimizer has left that region of parameter space upon convergence, which is indicated by an absence of the model convergence warnings described above. The following warnings indicate possibly-transient numerical problems with the fit, and can be treated in the same way (i.e. ignored if there are no errors or convergence warnings about the final fitted model). > Cholmod warning 'matrix not positive definite' In older versions of R (< 3.6.0): > Warning in f(par, order = order, ...) : value out of range in 'lgamma' ## false convergence This warning: > false convergence: the gradient ∇f(x) may be computed incorrectly, the other stopping tolerances may be too tight, or either f or ∇f may be discontinuous near the current iterate x comes from the `nlminb` optimizer used by default in `glmmTMB`. It's usually hard to diagnose the source of this warning (this [Stack Overflow answer](https://stackoverflow.com/questions/40039114/r-nlminb-what-does-false-convergence-actually-mean) explains a bit more about what it means). Reasonable methods for making sure your model is OK are: - restart the model at the estimated fitted values - try using a different optimizer, e.g. `control=glmmTMBControl(optimizer=optim, optArgs=list(method="BFGS"))` and see if the results are sufficiently similar to the original fit. # Errors ## NA/NaN gradient evaluation ```{r NA gradient, error=TRUE, warning=FALSE} dat1 = expand.grid(y=-1:1, rep=1:10) m1 = glmmTMB(y~1, dat1, family=nbinom2) ``` The error occurs here because the negative binomial distribution is inappropriate for data with negative values. If you see this error, check that the response variable meets the assumptions of the specified distribution. ## gradient length > Error in nlminb(start = par, objective = fn, gradient = gr) : gradient function must return a numeric vector of length x > Error in optimHess(par.fixed, obj$fn, obj$gr): gradient in optim evaluated to length x Try rescaling predictor variables. Try a simpler model and build up. (If you have a simple reproducible example of these errors, please post them to the issues list.) glmmTMB/vignettes/model_evaluation.Rnw0000644000176200001440000003740513616054060017612 0ustar liggesusers\documentclass[12pt]{article} %% vignette index specifications need to be *after* \documentclass{} %\VignetteEngine{knitr::knitr} %\VignetteIndexEntry{model evaluation} %\VignettePackage{glmmTMB} %\VignetteDepends{ggplot2} %\VignetteDepends{car} %\VignetteDepends{emmeans} %\VignetteDepends{effects} %\VignetteDepends{multcomp} %\VignetteDepends{MuMIn} %\VignetteDepends{DHARMa} %\VignetteDepends{broom} %\VignetteDepends{broom.mixed} %\VignetteDepends{dotwhisker} %\VignetteDepends{texreg} %\VignetteDepends{xtable} \usepackage{lineno} \usepackage[utf8]{inputenc} \usepackage{graphicx} \usepackage[american]{babel} %% for huxtable \usepackage{array} \usepackage{caption} \usepackage{graphicx} \usepackage{siunitx} \usepackage{colortbl} \usepackage{multirow} \usepackage{hhline} \usepackage{calc} \usepackage{tabularx} \usepackage{threeparttable} \usepackage{wrapfig} \newcommand{\R}{{\sf R}} \newcommand{\fixme}[1]{\textbf{\color{red} fixme: #1}} \newcommand{\notimpl}[1]{\emph{\color{magenta} #1}} \usepackage{url} \usepackage{hyperref} \usepackage{fancyvrb} \usepackage{natbib} %% \code{} below is not safe with \section{} etc. \newcommand{\tcode}[1]{{\tt #1}} \VerbatimFootnotes \bibliographystyle{chicago} %% need this for output of citation() below ... \newcommand{\bold}[1]{\textbf{#1}} %% code formatting %% https://tex.stackexchange.com/questions/273843/inline-verbatim-with-line-breaks-colored-font-and-highlighting/280212 % \usepackage{xcolor} %% see knit_hooks$set(...) below \newcommand\code[1]{\mytokenshelp#1 \relax\relax} \def\mytokenshelp#1 #2\relax{\allowbreak\grayspace\tokenscolor{#1}\ifx\relax#2\else \mytokenshelp#2\relax\fi} %\newcommand\tokenscolor[1]{\colorbox{gray!20}{\textcolor{blue}{% % \ttfamily\mystrut\smash{\detokenize{#1}}}}} \newcommand\tokenscolor[1]{\colorbox{gray!0}{\textcolor{black}{% \ttfamily\mystrut\smash{\detokenize{#1}}}}} \def\mystrut{\rule[\dimexpr-\dp\strutbox+\fboxsep]{0pt}{% \dimexpr\normalbaselineskip-2\fboxsep}} \def\grayspace{\hspace{0pt minus \fboxsep}} \title{Post-model-fitting procedures with \tcode{glmmTMB} models: diagnostics, inference, and model output} \date{\today} \author{} \begin{document} \maketitle %\linenumbers %% TO DO: pipeline for re-running stored objects <>= library("knitr") opts_chunk$set(fig.width=5,fig.height=5, out.width="0.8\\textwidth",echo=TRUE) ## https://tex.stackexchange.com/questions/148188/knitr-xcolor-incompatible-color-definition/254482 knit_hooks$set(document = function(x) {sub('\\usepackage[]{color}', '\\usepackage{xcolor}', x, fixed = TRUE)}) Rver <- paste(R.version$major,R.version$minor,sep=".") used.pkgs <- c("glmmTMB","bbmle") ## packages to report below @ The purpose of this vignette is to describe (and test) the functions in various downstream packages that are available for summarizing and otherwise interpreting glmmTMB fits. Some of the packages/functions discussed below may not be suitable for inference on parameters of the zero-inflation or dispersion models, but will be restricted to the conditional-mean model. <>= library(glmmTMB) library(car) library(emmeans) library(effects) library(multcomp) library(MuMIn) library(DHARMa) library(broom) library(broom.mixed) library(dotwhisker) library(ggplot2); theme_set(theme_bw()) library(texreg) library(xtable) library(huxtable) ## retrieve slow stuff L <- load(system.file("vignette_data","model_evaluation.rda", package="glmmTMB")) @ A couple of example models: % don't need to evaluate this since we have loaded owls_nb1 from model_evaluation.rda <>= owls_nb1 <- glmmTMB(SiblingNegotiation ~ FoodTreatment*SexParent + (1|Nest)+offset(log(BroodSize)), contrasts=list(FoodTreatment="contr.sum", SexParent="contr.sum"), family = nbinom1, zi = ~1, data=Owls) @ <>= data("cbpp",package="lme4") cbpp_b1 <- glmmTMB(incidence/size~period+(1|herd), weights=size,family=binomial, data=cbpp) ## simulated three-term Beta example set.seed(1001) dd <- data.frame(z=rbeta(1000,shape1=2,shape2=3), a=rnorm(1000),b=rnorm(1000),c=rnorm(1000)) simex_b1 <- glmmTMB(z~a*b*c,family=beta_family,data=dd) @ \section{model checking and diagnostics} \subsection{\tcode{DHARMa}} The \code{DHARMa} package provides diagnostics for hierarchical models. After running % set to eval=FALSE since we have this stored in model_evaluation.rda <>= owls_nb1_simres <- simulateResiduals(owls_nb1) @ you can plot the results: <>= plot(owls_nb1_simres) @ \subsubsection{issues} \begin{itemize} \item When you run \code{simulateResiduals()} you'll notice a long warning (actually a \emph{message}: ``It seems you are diagnosing a \code{glmmTMB} model \ldots'' that explains some issues with \code{glmmTMB} fits in \code{DHARMa} \item \code{DHARMa} will only work for models using families for which a simulate method has been implemented (in \code{TMB}, and appropriately reflected in \code{glmmTMB}) \end{itemize} \section{Inference} \subsection{\tcode{car::Anova}} We can use \code{car::Anova()} to get traditional ANOVA-style tables from \code{glmmTMB} fits. A few limitations/reminders: \begin{itemize} \item these tables use Wald $\chi^2$ statistics for comparisons (neither likelihood ratio tests nor $F$ tests) \item they apply to the fixed effects of the conditional component of the model only (other components \emph{might} work, but haven't been tested at all) \item as always, if you want to do type 3 tests, you should probably set sum-to-zero contrasts on factors and center numerical covariates (see contrasts argument above) \end{itemize} <>= if (requireNamespace("car") && getRversion() >= "3.6.0") { Anova(owls_nb1) ## default type II Anova(owls_nb1,type="III") } @ \subsection{effects} <>= effects_ok <- (requireNamespace("effects") && getRversion() >= "3.6.0") if (effects_ok) { (ae <- allEffects(owls_nb1)) plot(ae) } @ (the error can probably be ignored) <>= if (effects_ok) { plot(allEffects(simex_b1)) } @ \subsection{\tcode{emmeans}} <>= emmeans(owls_nb1, poly ~ FoodTreatment | SexParent) @ \subsection{\tcode{drop1}} \code{stats::drop1} is a built-in R function that refits the model with various terms dropped. In its default mode it respects marginality (i.e., it will only drop the top-level interactions, not the main effects): <>= system.time(owls_nb1_d1 <- drop1(owls_nb1,test="Chisq")) @ <>= print(owls_nb1_d1) @ In principle, using \code{scope = . ~ . - (1|Nest)} should work to execute a ``type-3-like'' series of tests, dropping the main effects one at a time while leaving the interaction in (we have to use \code{- (1|Nest)} to exclude the random effects because \code{drop1} can't handle them). However, due to the way that R handles formulas, dropping main effects from an interaction of *factors* has no effect on the overall model. (It would work if we were testing the interaction of continuous variables.) \subsubsection{issues} The \code{mixed} package implements a true ``type-3-like'' parameter-dropping mechanism for \code{[g]lmer} models. Something like that could in principle be applied here. \subsection{Model selection and averaging with \tcode{MuMIn}} We can run \code{MuMIn::dredge(owls_nb1)} on the model to fit all possible submodels. Since this takes a little while (45 seconds or so), we've instead loaded some previously computed results: % stored in vignette_data/model_evaluation.rda ... <>= owls_nb1_dredge @ <>= op <- par(mar=c(2,5,14,3)) plot(owls_nb1_dredge) par(op) ## restore graphics parameters @ Model averaging: <>= model.avg(owls_nb1_dredge) @ \subsubsection{issues} \begin{itemize} \item may not work for Beta models because the \code{family} component ("beta") is not identical to the name of the family function (\code{beta_family()})? (Kamil Bartoń, pers. comm.) \end{itemize} \subsection{\tcode{multcomp} for multiple comparisons and \emph{post hoc} tests} <>= glht_glmmTMB <- function (model, ..., component="cond") { glht(model, ..., coef. = function(x) fixef(x)[[component]], vcov. = function(x) vcov(x)[[component]], df = NULL) } modelparm.glmmTMB <- function (model, coef. = function(x) fixef(x)[[component]], vcov. = function(x) vcov(x)[[component]], df = NULL, component="cond", ...) { multcomp:::modelparm.default(model, coef. = coef., vcov. = vcov., df = df, ...) } @ <>= g1 <- glht(cbpp_b1, linfct = mcp(period = "Tukey")) summary(g1) @ \subsubsection{issues} It is possible to make \code{multcomp} work in a way that (1) actually uses the S3 method structure and (2) doesn't need access to private multcomp methods (i.e. accessed by \code{multcomp:::}) ? Not sure, but both of the following hacks should work. (The \code{glht_glmmTMB} solution below is clunky because it isn't a real S3 method; the \code{model.parm.glmmTMB} solution can't be included in the package source code as-is because ::: is not allowed in CRAN package code.) \section{Extracting coefficients, coefficient plots and tables} \subsection{\tcode{broom} and friends} The \code{broom} and \code{broom.mixed} packages are designed to extract information from a broad range of models in a convenient (tidy) format; the dotwhisker package builds on this platform to draw elegant coefficient plots. <>= if (requireNamespace("broom.mixed") && requireNamespace("dotwhisker")) { (t1 <- broom.mixed::tidy(owls_nb1, conf.int = TRUE)) if (packageVersion("dotwhisker")>"0.4.1") { ## to get this version (which fixes various dotwhisker problems) ## use devtools::install_github("bbolker/broom.mixed") or ## wait for pull request acceptance/submission to CRAN/etc. dwplot(owls_nb1)+geom_vline(xintercept=0,lty=2) } else { owls_nb1$coefficients <- TRUE ## hack! dwplot(owls_nb1,by_2sd=FALSE)+geom_vline(xintercept=0,lty=2) } } @ \subsubsection{issues} (these are more general \code{dwplot} issues) \begin{itemize} \item use black rather than color(1) when there's only a single model, i.e. only add aes(colour=model) conditionally? - draw points even if std err / confint are NA (draw \code{geom_point()} as well as \code{geom_pointrange()}? need to apply all aesthetics, dodging, etc. to both ...) \item for glmmTMB models, allow labeling by component? or should this be done by manipulating the tidied frame first? (i.e.: \code{tidy(.) \%>\% tidyr::unite(term,c(component,term))}) \end{itemize} \subsection{coefficient tables with \tcode{xtable}} The \code{xtable} package can output data frames as \LaTeX\ tables; this isn't quite as elegant as \code{stargazer} etc., but is not a bad start. I've sprinkled lots of hard line-breaks, spaces, and newlines in below: someone who was better at \TeX\ could certainly do a better job. (\code{xtable} can also produce HTML output.) <>= ss <- summary(owls_nb1) ## print table; add space, pxt <- function(x,title) { cat(sprintf("{\n\n\\textbf{%s}\n\\ \\\\\\vspace{2pt}\\ \\\\\n",title)) print(xtable(x), floating=FALSE); cat("\n\n") cat("\\ \\\\\\vspace{5pt}\\ \\\\\n") } <>= pxt(lme4::formatVC(ss$varcor$cond),"random effects variances") pxt(coef(ss)$cond,"conditional fixed effects") pxt(coef(ss)$zi,"conditional zero-inflation effects") @ <>= if (requireNamespace("xtable")) { pxt(lme4::formatVC(ss$varcor$cond),"random effects variances") pxt(coef(ss)$cond,"conditional fixed effects") pxt(coef(ss)$zi,"conditional zero-inflation effects") } @ \subsection{coefficient tables with \tcode{texreg}} <>= source(system.file("other_methods","extract.R",package="glmmTMB")) texreg(owls_nb1,caption="Owls model", label="tab:owls") @ See output in Table~\ref{tab:owls}. \subsection{coefficient tables with \tcode{huxtable}} The \code{huxtable} package allows output in either \LaTeX\ or HTML: this example is tuned for \LaTeX. <>= cc <- c("intercept (mean)"="(Intercept)", "food treatment (starvation)"="FoodTreatment1", "parental sex (M)"="SexParent1", "food $\\times$ sex"="FoodTreatment1:SexParent1") h0 <- huxreg(" "=owls_nb1, # give model blank name so we don't get '(1)' tidy_args=list(effects="fixed"), coefs=cc, error_pos="right", statistics="nobs" # don't include logLik and AIC ) names(h0)[2:3] <- c("estimate","std. err.") ## allow use of math notation in name h1 <- set_cell_properties(h0,row=5,col=1,escape_contents=FALSE) cat(to_latex(h1,tabular_only=TRUE)) @ \subsubsection{issues} \begin{itemize} \item \code{huxtable} needs quite a few additional \LaTeX\ packages: use \code{report_latex_dependencies()} to see what they are. \end{itemize} \section{influence measures} \emph{Influence measures} quantify the effects of particular observations, or groups of observations, on the results of a statistical model; \emph{leverage} and \emph{Cook's distance} are the two most common formats for influence measures. If a \href{https://en.wikipedia.org/wiki/Projection_matrix}{projection matrix} (or ``hat matrix'') is available, influence measures can be computed efficiently; otherwise, the same quantities can be estimated by brute-force methods, refitting the model with each group or observation successively left out. We've adapted the \tcode{car::influence.merMod} function to handle \tcode{glmmTMB} models; because it uses brute force, it can be slow, especially if evaluating the influence of individual observations. For now, it is included as a separate source file rather than exported as a method (see below), although it may be included in the package (or incorporated in the \tcode{car} package) in the future. <>= source(system.file("other_methods","influence_mixed.R", package="glmmTMB")) @ <>= owls_nb1_influence_time <- system.time( owls_nb1_influence <- influence_mixed(owls_nb1, groups="Nest") ) @ Re-fitting the model with each of the \Sexpr{length(unique(Owls$Nest))} nests excluded takes \Sexpr{round(owls_nb1_influence_time[["elapsed"]])} seconds (on an old Macbook Pro). The \tcode{car::infIndexPlot()} function is one way of displaying the results: <>= car::infIndexPlot(owls_nb1_influence) @ Or, you can transform the results and plot them however you like: <>= inf <- as.data.frame(owls_nb1_influence[["fixed.effects[-Nest]"]]) inf <- transform(inf, nest=rownames(inf), cooks=cooks.distance(owls_nb1_influence)) inf$ord <- rank(inf$cooks) if (require(reshape2)) { inf_long <- melt(inf, id.vars=c("ord","nest")) gg_infl <- (ggplot(inf_long,aes(ord,value)) + geom_point() + facet_wrap(~variable, scale="free_y") + scale_x_reverse(expand=expand_scale(mult=0.15)) + scale_y_continuous(expand=expand_scale(mult=0.15)) + geom_text(data=subset(inf_long,ord>24), aes(label=nest),vjust=-1.05) ) print(gg_infl) } @ \section{to do} \begin{itemize} \item more plotting methods (\code{sjplot}) \item output with \code{memisc} \item AUC etc. with \code{ModelMetrics} \end{itemize} <>= ## store time-consuming stuff save("owls_nb1", "owls_nb1_simres", "owls_nb1_dredge", "owls_nb1_influence", "owls_nb1_influence_time", file="../inst/vignette_data/model_evaluation.rda", version=2 ## for compatibility with R < 3.6.0 ) @ \end{document} glmmTMB/vignettes/miscEx.rmd0000644000176200001440000000161513614324717015530 0ustar liggesusers--- title: "Miscellaneous examples" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{miscellaneous examples} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- ```{r echo=FALSE} library(glmmTMB) ``` ## Beta dispersion model ```{r simbeta1} set.seed(1001) N <- 1000 mean_pars <- c(1,2) disp_pars <- c(1,2) dd <- data.frame(x=rnorm(N)) m <- plogis(mean_pars[1]+mean_pars[2]*dd$x) d <- exp(disp_pars[1]+disp_pars[2]*dd$x) dd$y <- rbeta(N,shape1=m*d,shape2=(1-m)*d) ``` Fit models: ```{r modbeta1} ## location only m1 <- glmmTMB(y~x, family=beta_family(), data=dd) ## add model for dispersion m2 <- update(m1,dispformula=~x) ``` Fixed effects look close to theoretical values: ```{r coefbeta1} fixef(m2) ``` AIC is insanely much better for the model with dispersion varying: ```{r AICbeta1} bbmle::AICtab(m1,m2) ``` glmmTMB/vignettes/glmmTMB.Rnw0000644000176200001440000003502013614324717015560 0ustar liggesusers\documentclass[12pt]{article} %% vignette index specifications need to be *after* \documentclass{} %\VignetteEngine{knitr::knitr} %\VignetteIndexEntry{basic examples of glmmTMB usage} %\VignettePackage{glmmTMB} %\VignetteDepends{ggplot2} %\VignetteDepends{grid} %\VignetteDepends{bbmle} %\VignetteDepends{mlmRev} %\usepackage{lineno} \usepackage[utf8]{inputenc} \usepackage{graphicx} \usepackage[american]{babel} \newcommand{\R}{{\sf R}} \newcommand{\fixme}[1]{\textbf{\color{red} fixme: #1}} \newcommand{\notimpl}[1]{\emph{\color{magenta} #1}} \usepackage{url} \usepackage{hyperref} \usepackage{alltt} \newcommand{\code}[1]{{\tt #1}} \usepackage{fancyvrb} \usepackage{natbib} \VerbatimFootnotes \bibliographystyle{chicago} %% need this for output of citation() below ... \newcommand{\bold}[1]{\textbf{#1}} \title{Getting started with the \code{glmmTMB} package} \author{Ben Bolker} \date{\today} \begin{document} \maketitle %\linenumbers <>= library("knitr") opts_chunk$set(fig.width=5,fig.height=5, out.width="0.8\\textwidth",echo=TRUE) Rver <- paste(R.version$major,R.version$minor,sep=".") used.pkgs <- c("glmmTMB","bbmle") ## packages to report below @ \section{Introduction/quick start} \code{glmmTMB} is an R package built on the \href{https://github.com/kaskr/adcomp}{Template Model Builder} automatic differentiation engine, for fitting generalized linear mixed models and extensions. (Not-yet-implemented features are denoted \notimpl{like this}) \begin{itemize} \item response distributions: Poisson, binomial, negative binomial (NB1 and NB2 parameterizations), Gamma, Beta, Gaussian; truncated Poisson and negative binomial; \notimpl{Student $t$; Tweedie} \item link functions: log, logit, probit, complementary log-log, inverse, identity \item zero-inflation with fixed and random-effects components; hurdle models via truncated Poisson/NB \item single or multiple (nested or crossed) random effects \item offsets \item fixed-effects models for dispersion \item diagonal, compound-symmetric, or unstructured random effects variance-covariance matrices; first-order autoregressive (AR1) variance structures \end{itemize} In order to use \code{glmmTMB} effectively you should already be reasonably familiar with generalized linear mixed models (GLMMs), which in turn requires familiarity with (i) generalized linear models (e.g. the special cases of logistic, binomial, and Poisson regression) and (ii) `modern' mixed models (those working via maximization of the marginal likelihood rather than by manipulating sums of squares). \cite{bolker_generalized_2009} and \cite{bolker_glmm_2014} are reasonable starting points in this area (especially geared to biologists and less-technical readers), as are \cite{zuur_mixed_2009}, \cite{millar_maximum_2011}, and \cite{zuur_beginners_2013}. In order to fit a model in \code{glmmTMB} you need to: \begin{itemize} \item specify a model for the conditional effects, in the standard R (Wilkinson-Rogers) formula notation (see \code{?formula} or Section 11.1 of the \href{http://cran.r-project.org/doc/manuals/R-intro.pdf}{Introduction to R}. Formulae can also include \emph{offsets}. \item specify a model for the random effects, in the notation that is common to the \code{nlme} and \code{lme4} packages. Random effects are specified as \code{x|g}, where \code{x} is an effect and \code{g} is a grouping factor (which must be a factor variable, or a nesting of/interaction among factor variables). For example, the formula would be \code{1|block} for a random-intercept model or \code{time|block} for a model with random variation in slopes through time across groups specified by \code{block}. A model of nested random effects (block within site) would be \code{1|site/block}; a model of crossed random effects (block and year) would be \code{(1|block)+(1|year)}. \item choose the error distribution by specifying the family (\code{family} argument). In general, you can specify the function (\code{binomial}, \code{gaussian}, \code{poisson}, \code{Gamma} from base R, or one of the options listed at \code{family\_glmmTMB} [\code{nbinom2}, \code{beta\_family()}, \code{betabinomial}, \ldots])). \item choose the error distribution by specifying the family (\code{family} argument). For standard GLM families implemented in R, you can use the function name (\code{binomial}, \code{gaussian}, \code{poisson}, \code{Gamma}). Otherwise, you should specify the family argument as a list containing (at least) the (character) elements \code{family} and \code{link}, e.g. \code{family=list(family="nbinom2",link="log")}. \item optionally specify a zero-inflation model (via the \code{ziformula} argument) with fixed and/or random effects \item optionally specify a dispersion model with fixed effects \end{itemize} This document was generated using \Sexpr{R.version$version.string} and package versions: <>= pkgver <- vapply(sort(used.pkgs),function(x) as.character(packageVersion(x)),"") print(pkgver,quote=FALSE) @ The current citation for \code{glmmTMB} is: \begin{quote} %% fixme: would like to deal with smart quotes <>= print(citation("glmmTMB"),style="latex") @ \end{quote} \section{Preliminaries: packages and data} Load required packages: <>= library("glmmTMB") library("bbmle") ## for AICtab library("ggplot2") ## cosmetic theme_set(theme_bw()+ theme(panel.spacing=grid::unit(0,"lines"))) @ The data, taken from \cite{zuur_mixed_2009} and ultimately from \cite{roulinbersier_2007}, quantify the number of negotiations among owlets (owl chicks) in different nests \emph{prior} to the arrival of a provisioning parent as a function of food treatment (deprived or satiated), the sex of the parent, and arrival time. The total number of calls from the nest is recorded, along with the total brood size, which is used as an offset to allow the use of a Poisson response. Since the same nests are measured repeatedly, the nest is used as a random effect. The model can be expressed as a zero-inflated generalized linear mixed model (ZIGLMM). Various small manipulations of the data set: (1) reorder nests by mean negotiations per chick, for plotting purposes; (2) add log brood size variable (for offset); (3) rename response variable and abbreviate one of the input variables. %% FIXME: I get a warning message ("NAs introduced by coercion") here, but only in knitr, %% and not on a clean start ... ? %% some weird package interaction ? <>= Owls <- transform(Owls, Nest=reorder(Nest,NegPerChick), NCalls=SiblingNegotiation, FT=FoodTreatment) @ (If you were really using this data set you should start with \code{summary(Owls)} to explore the data set.) % fig.cap="Basic view of owl data from \\cite{roulinbersier_2007}." <>= G0 <- ggplot(Owls,aes(x=reorder(Nest,NegPerChick), y=NegPerChick))+ labs(x="Nest",y="Negotiations per chick")+coord_flip()+ facet_grid(FoodTreatment~SexParent) G0+stat_sum(aes(size=..n..),alpha=0.5)+ scale_size_continuous(name="# obs", breaks=seq(1,9,by=2))+ theme(axis.title.x=element_text(hjust=0.5,size=12), axis.text.y=element_text(size=7)) @ We should explore the data before we start to build models, e.g. by plotting it in various ways, but this vignette is about \code{glmmTMB}, not about data visualization \ldots Now fit some models: The basic \code{glmmTMB} fit --- a zero-inflated Poisson model with a single zero-inflation parameter applying to all observations (\verb+ziformula~1+). (Excluding zero-inflation is \code{glmmTMB}'s default: to exclude it explicitly, use \verb+ziformula~0+.) <>= gt1 <- system.time(glmmTMB(NCalls~(FT+ArrivalTime)*SexParent+ offset(log(BroodSize))+(1|Nest), ziformula=~1, data=Owls, family=poisson)) @ <>= fit_zipoisson <- glmmTMB(NCalls~(FT+ArrivalTime)*SexParent+ offset(log(BroodSize))+(1|Nest), data=Owls, ziformula=~1, family=poisson) @ <>= summary(fit_zipoisson) @ We can also try a standard zero-inflated negative binomial model; the default is the ``NB2'' parameterization (variance = $\mu(1+\mu/k)$: \cite{hardin_generalized_2007}). To use families (Poisson, binomial, Gaussian) that are defined in \R, you should specify them as in \code{?glm} (as a string referring to the family function, as the family function itself, or as the result of a call to the family function: i.e. \code{family="poisson"}, \code{family=poisson}, \code{family=poisson()}, and \code{family=poisson(link="log")} are all allowed and all equivalent (the log link is the default for the Poisson family). Some of the additional families that are \emph{not} defined in base R (at this point \code{nbinom2} and \code{nbinom1}) can be specified using the same format. Otherwise, for families that are implemented in \code{glmmTMB} but for which \code{glmmTMB} does not provide a function, you should specify the \code{family} argument as a list containing (at least) the (character) elements \code{family} and \code{link}, e.g. \code{family=list(family="nbinom2",link="log")}. (In order to be able to retrieve Pearson (variance-scaled) residuals from a fit, you also need to specify a \code{variance} component; see \code{?family\_glmmTMB}.) <>= fit_zinbinom <- update(fit_zipoisson,family=nbinom2) @ %% FIXME: caching may lead to %% ## Error in ICtab(..., mnames = mnames, type = "AIC"): memory block of size 3.1 Gb %% downstream, in AICtab() ... %% for now I'm removing caching, but we should %% (1) document this as an issue/make a MWE %% (2) fix it %% (3) we could also cache the AICtab chunk as well .. Alternatively, we can use an ``NB1'' fit (variance = $\phi \mu$). <>= fit_zinbinom1 <- update(fit_zipoisson,family=nbinom1) @ \notimpl{we should have a \code{getFamily} function: ideally it would also specify which are really implemented (although that's harder), and specify default links} Relax the assumption that total number of calls is strictly proportional to brood size (i.e. using log(brood size) as an offset): <>= fit_zinbinom1_bs <- update(fit_zinbinom1, . ~ (FT+ArrivalTime)*SexParent+ BroodSize+(1|Nest)) @ Every change we have made so far improves the fit --- changing distributions improves it enormously, while changing the role of brood size makes only a modest (-1 AIC unit) difference: <>= AICtab(fit_zipoisson,fit_zinbinom,fit_zinbinom1,fit_zinbinom1_bs) @ \subsection{Hurdle models} In contrast to zero-inflated models, hurdle models treat zero-count and non-zero outcomes as two completely separate categories, rather than treating the zero-count outcomes as a mixture of structural and sampling zeros. \code{glmmTMB} includes truncated Poisson and negative binomial familes and hence can fit hurdle models. <>= fit_hnbinom1 <- update(fit_zinbinom1_bs, ziformula=~., data=Owls, family=list(family="truncated_nbinom1",link="log")) @ Then we can use \code{AICtab} to compare all the models. <>= AICtab(fit_zipoisson,fit_zinbinom,fit_zinbinom1,fit_zinbinom1_bs,fit_hnbinom1) @ \section{Sample timings} To get a rough idea of \code{glmmTMB}'s speed relative to \code{lme4} (the most commonly used mixed-model package for R), we try a few standard problems, enlarging the data sets by cloning the original data set (making multiple copies and sticking them together). <>= data("Contraception",package="mlmRev") nc <- nrow(Contraception) nl <- length(levels(Contraception$district)) load("contraceptionTimings.rda") meandiff <- mean(with(tmatContraception, time[pkg=="glmer"]/time[pkg=="glmmTMB"])) @ Figure~\ref{fig:contraception} shows the results of replicating the \code{Contraception} data set (\Sexpr{nc} observations, \Sexpr{nl} levels in the random effects grouping level) from 1 to 40 times. \code{glmmADMB} is sufficiently slow ($\approx 1$ minute for a single copy of the data) that we didn't try replicating very much. On average, \code{glmmTMB} is about \Sexpr{round(meandiff,1)} times faster than \code{glmer} for this problem. <>= ggplot(tmatContraception,aes(n,time,colour=pkg))+geom_point()+ scale_y_log10(breaks=c(1,2,5,10,20,50,100))+ scale_x_log10(breaks=c(1,2,4,10,20,40))+ labs(x="Replication (x 1934 obs.)",y="Elapsed time (s)")+ geom_smooth(method="lm")+ scale_colour_brewer(palette="Set1") @ <>= load("InstEvalTimings.rda") n_InstEval <- 73421L ## seems silly to require lme4 just to get this number meandiff_inst2 <- with(tmatInstEval, time[pkg=="lmer"]/time[pkg=="glmmTMB"]) ggplot(tmatInstEval,aes(n,time,colour=pkg))+geom_point()+ scale_y_log10(breaks=c(1,2,5,10,20,50,100,200))+ scale_x_log10(breaks=c(0.1,0.2,0.5,1.0))+ labs(x=sprintf("Replication (x %d obs.)",n_InstEval), y="Elapsed time (s)")+ geom_smooth(method="lm")+ scale_colour_brewer(palette="Set1") @ Figure~\ref{fig:insteval} shows equivalent timings for the \code{InstEval} data set, although in this case since the original data set is large (\Sexpr{n_InstEval} observations) we subsample the data set rather than cloning it: in this case, the advantage is reversed and \code{lmer} is about \Sexpr{round(1/mean(meandiff_inst2,1))} times faster. In general, we expect \code{glmmTMB}'s advantages over \code{lme4} to be (1) greater flexibility (zero-inflation etc.); (2) greater speed for GLMMs, especially those with large number of ``top-level'' parameters (fixed effects plus random-effects variance-covariance parameters). In contrast, \code{lme4} should be faster for LMMs (for maximum speed, you may want to check the \href{https://github.com/dmbates/MixedModels.jl}{MixedModels.jl} package for Julia); \code{lme4} is more mature and at present has a wider variety of diagnostic checks and methods for using model results, including downstream packages. \bibliography{glmmTMB} \end{document} glmmTMB/vignettes/contraceptionTimings.rda0000644000176200001440000000155413614324717020471 0ustar liggesusersՔkSgǟ5V]lu]ig+mQfju]a6jR_(?T:a"E"CqcSĈ ' 4}^s۴lB(¤(B1q/A$4>^M׻}B>冴LAl-$dBRt$L"sS'"iD$d,2$d\IMb#gz2){9u{z5WSV{ħII:m<%{7޺_^irܳnREʃE6]^K3a;:Y^]Ͽx` gWg yxbP_s׾a[B˹`@ACfNvml`kܜ8HI_jV۱bAuҗ%+\]@g}vDe|/STTKjj?lWaIy 8: TRϵ >4PxAT9w@DuL0n0iws( %\VignetteIndexEntry{simulate} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- `glmmTMB` has the capability to simulate from a fitted model. These simulations resample random effects from their estimated distribution. In future versions of `glmmTMB`, it may be possible to condition on estimated random effects. ```{r setup, include=FALSE, message=FALSE} library(knitr) knitr::opts_chunk$set(echo = TRUE) ``` ```{r libs,message=FALSE} library(glmmTMB) library(ggplot2); theme_set(theme_bw()) ``` Fit a typical model: ```{r fit1} data(Owls) owls_nb1 <- glmmTMB(SiblingNegotiation ~ FoodTreatment*SexParent + (1|Nest)+offset(log(BroodSize)), family = nbinom1, ziformula = ~1, data=Owls) ``` Then we can simulate from the fitted model with the `simulate.glmmTMB` function. It produces a list of simulated observation vectors, each of which is the same size as the original vector of observations. The default is to only simulate one vector (`nsim=1`) but we still return a list for consistency. ```{r sim} simo=simulate(owls_nb1, seed=1) Simdat=Owls Simdat$SiblingNegotiation=simo[[1]] Simdat=transform(Simdat, NegPerChick = SiblingNegotiation/BroodSize, type="simulated") Owls$type = "observed" Dat=rbind(Owls, Simdat) ``` Then we can plot the simulated data against the observed data to check if they are similar. ```{r plots,fig.width=7} ggplot(Dat, aes(NegPerChick, colour=type))+geom_density()+facet_grid(FoodTreatment~SexParent) ``` glmmTMB/vignettes/mcmc.rmd0000644000176200001440000001151313614324717015215 0ustar liggesusers--- title: "post-hoc MCMC with glmmTMB" author: "Ben Bolker" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{post-hoc MCMC} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- One commonly requested feature is to be able to run a *post hoc* Markov chain Monte Carlo analysis based on the results of a frequentist fit. This is often a reasonable shortcut for computing confidence intervals and p-values that allow for finite-sized samples rather than relying on asymptotic sampling distributions. This vignette shows an example of such an analysis. Some caveats: - when using such a "pseudo-Bayesian" approach, be aware that using a scaled likelihood (implicit, improper priors) can often cause problems, especially when the model is poorly constrained by the data - in particular, models with poorly constrained random effects (singular or nearly singular) are likely to give bad results - as shown below, even models that are well-behaved for frequentist fitting may need stronger priors to give well-behaved MCMC results - as with all MCMC analysis, it is the *user's responsibility to check for proper mixing and convergence of the chains* before drawing conclusions - the first MCMC sampler illustrated below is simple (Metropolis with a multivariate normal candidate distribution). Users who want to do MCMC sampling on a regular basis should consider the [tmbstan package](https://CRAN.R-project.org/package=tmbstan), which does much more efficient hybrid/Hamiltonian Monte Carlo sampling. ```{r knitr_setup, include=FALSE, message=FALSE} library(knitr) opts_chunk$set(echo = TRUE) rc <- knitr::read_chunk rc(system.file("vignette_data","mcmc.R",package="glmmTMB")) ``` Load packages: ```{r libs,message=FALSE} library(glmmTMB) library(coda) ## MCMC utilities library(reshape2) ## for melt() ## graphics library(lattice) library(ggplot2); theme_set(theme_bw()) ``` Fit basic model: ```{r fit1} ``` Set up for MCMC: define scaled log-posterior function (in this case the log-likelihood function); extract coefficients and variance-covariance matrices as starting points. ```{r setup} ``` This is a basic block Metropolis sampler, based on Florian Hartig's code [here](https://theoreticalecology.wordpress.com/2010/09/17/metropolis-hastings-mcmc-in-r/). ```{r run_MCMC} ``` Run the chain: ```{r do_run_MCMC,eval=FALSE} ``` ```{r load_MCMC, echo=FALSE} L <- load(system.file("vignette_data", "mcmc.rda", package="glmmTMB")) ``` (running this chain takes `r round(t1["elapsed"],1)` seconds) Add more informative names and transform correlation parameter (see vignette on covariance structures and parameters): ```{r add_names} colnames(m1) <- c(names(fixef(fm1)[[1]]), "log(sigma)", c("log(sd_Intercept)","log(sd_Days)","cor")) m1[,"cor"] <- sapply(m1[,"cor"],get_cor) ``` ```{r traceplot,fig.width=7} xyplot(m1,layout=c(2,3),asp="fill") ``` The trace plots are poor, especially for the correlation; the effective sample size backs this up, as would any other diagnostics we did. ```{r effsize} print(effectiveSize(m1),digits=3) ``` **In a real analysis we would stop and fix the mixing/convergence problems before proceeding**; for this simple sampler, some of our choices would be (1) simply run the chain for longer; (2) tune the candidate distribution (e.g. by using `tune` to scale some parameters, or perhaps by switching to a multivariate Student t distribution [see the `mvtnorm` package]); (3) add regularizing priors. Ignoring the problems and proceeding, we can compute column-wise quantiles or highest posterior density intervals (`coda::HPDinterval`) to get confidence intervals. Plotting posterior distributions, omitting the intercept because it's on a very different scale. ```{r violins,echo=FALSE} ggplot(reshape2::melt(as.matrix(m1[,-1])),aes(x=Var2,y=value))+ geom_violin(fill="gray")+coord_flip()+labs(x="") ``` ## tmbstan The `tmbstan` package allows direct, simple access to a hybrid/Hamiltonian Monte Carlo algorithm for sampling from a TMB object; the `$obj` component of a `glmmTMB` fit is such an object. (To run this example you'll need to install the `tmbstan` package and its dependencies.) ```{r do_tmbstan,eval=FALSE} ``` (running this command, which creates 4 chains, takes `r round(t2["elapsed"],1)` seconds) However, there are many indications (warning messages; trace plots) that the correlation parameter needs to a more informative prior. (In the plot below, the correlation parameter is shown on its unconstrained scale; the actual correlation would be $\theta_3/\sqrt{1+\theta_3^2}$.) ```{r show_traceplot,echo=FALSE,fig.width=8,fig.height=5} library(png) library(grid) img <- readPNG(system.file("vignette_data","tmbstan_traceplot.png",package="glmmTMB")) grid.raster(img) ``` ## To do - solve mixing for cor parameter - more complex example - e.g. `Owls` glmmTMB/vignettes/timingInstEval.R0000644000176200001440000000136413614324717016655 0ustar liggesuserssource("timingFuns.R") data(InstEval,package="lme4") library("lme4") library("glmmTMB") library("plyr") ## load before dplyr; for ldply() library("tidyr") library("dplyr") ## make sure this is run with optimized build of glmmTMB, ## i.e. "make install" rather than "make quick-install/quick-check" ## (or at least document) nvals <- seq(0.1,1,by=0.1) form <- y~service+lectage+studage+(1|d)+(1|s)+(1|dept) tmat <- ldply(nvals,getTimes,basedata=InstEval, form=form,family=NULL,which=c("glmmTMB","lmer"), .progress="text") ## reshape: wide-to-long, add n values ff <- function(dd,n=seq(nrow(dd))) { mutate(dd,n=nvals) %>% gather(pkg,time,-n) } tmatInstEval <- ff(tmat) save("tmatInstEval",file="InstEvalTimings.rda") glmmTMB/vignettes/timingFuns.R0000644000176200001440000000171413614324717016042 0ustar liggesusers ## run model & extract elapsed time form0 <- use ~ urban+age+livch+(urban|district) tt <- function(fun,data,form=form0,family=binomial,debug=FALSE) { argList <- list(form,data) if (!is.null(family)) argList <- c(argList,list(family=family)) unname(system.time(do.call(fun,argList))["elapsed"]) } ## replicate timings across models for a specified expansion ## of the data getTimes <- function(n=1,which=c("glmmTMB","glmer"), basedata=Contraception,form=form0, family=binomial, debug=FALSE) { if (n>1) { if (!n==round(n)) stop("only integer magnification allowed") ## replicate data c2 <- do.call(rbind,replicate(n,basedata,simplify=FALSE)) } else { ## subsample c2 <- basedata[sample.int(round(n*nrow(basedata))),] } ## run for all models res <- setNames(vapply(which,tt,data=c2,form=form, family=family,numeric(1)),which) return(res) } glmmTMB/vignettes/timingContraception.R0000644000176200001440000000156113614324717017737 0ustar liggesuserssource("timingFuns.R") data(Contraception,package="mlmRev") library("lme4") library("glmmTMB") library("glmmADMB") library("plyr") ## load before dplyr; for ldply() library("tidyr") library("dplyr") ## make sure this is run with optimized build of glmmTMB, ## i.e. "make install" rather than "make quick-install/quick-check" ## (or at least document) nmax <- 40 ## max replications for glmer/glmmTMB nmaxADMB <- 2 ## max reps for glmmadmb (much slower) ## slow enough that I should consider using something with ## checkpointing instead ... tmat <- ldply(seq(nmax),getTimes) tmatADMB <- ldply(seq(nmaxADMB),getTimes,which="glmmadmb") ## reshape: wide-to-long, add n values ff <- function(dd,n=seq(nrow(dd))) { mutate(dd,n=n) %>% gather(pkg,time,-n) } tmatContraception <- rbind(ff(tmat),ff(tmatADMB)) save("tmatContraception",file="contraceptionTimings.rda") glmmTMB/vignettes/glmmTMB.bib0000644000176200001440000001426013614324717015551 0ustar liggesusers @Book{Bolker2008, author = {Benjamin M. Bolker}, title = {Ecological Models and Data in R}, publisher = {Princeton University Press}, year = {2008}, address = {Princeton, NJ} } @article{roulinbersier_2007, title = {Nestling barn owls beg more intensely in the presence of their mother than in the presence of their father}, author = {Roulin, A. and L. Bersier}, year = {2007}, journal= {Animal Behaviour}, volume= {74}, pages={1099-1106}, url = {http://www.sciencedirect.com/science/article/B6W9W-4PK8B6H-8/2/e43cfbaad4dc0bb2207adfc54a460c89} } @book{zuur_mixed_2009, edition = {1}, title = {Mixed Effects Models and Extensions in Ecology with R}, isbn = {0387874577}, publisher = {Springer}, author = {Zuur, Alain F. and Ieno, Elena N. and Walker, Neil J. and Saveliev, Anatoly A. and Smith, Graham M.}, month = mar, year = {2009} } @ARTICLE{Fournieretal11, author = {David A. Fournier and Hans J. Skaug and Johnoel Ancheta and Jim Ianellid and Arni Magnusson and Mark Maunder and Anders Nielsen and John Sibert}, year = {2011}, title = {AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models}, journal = {Optimization Methods \& Software}, volume = {00}, number = {0}, pages = {1–17} } @article{bolker_generalized_2009, title = {Generalized linear mixed models: a practical guide for ecology and evolution}, volume = {24}, issn = {0169-5347}, shorttitle = {Generalized linear mixed models}, url = {http://www.sciencedirect.com/science/article/B6VJ1-4VGKHJP-1/2/35970065c78c14ad30bf71bd1d5b452e}, doi = {10.1016/j.tree.2008.10.008}, abstract = {How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models {(GLMMs)} provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on {GLMMs} in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating {GLMM} parameters in simple cases, complex {GLMMs} are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of {GLMMs} in ecology and evolution, discuss estimation and inference and summarize [`]best-practice' data analysis procedures for scientists facing this challenge.}, journal = {Trends in Ecology \& Evolution}, author = {Bolker, Benjamin M. and Brooks, Mollie E. and Clark, Connie J. and Geange, Shane W. and Poulsen, John R. and Stevens, M. Henry H. and White, {Jada-Simone} S.}, year = {2009}, pages = {127--135} } @InCollection{bolker_glmm_2014, author = {Benjamin M. Bolker}, editor = {Fox, Gordon A. and Negrete-Yankelevich, Simoneta and Sosa, Vinicio J.}, booktitle = {Ecological Statistics: Contemporary theory and application}, title = {Linear and Generalized Linear Mixed Models}, publisher = {Oxford University Press}, year = {2015}, chapter = {13}, isbn = {978-0-19-967255-4} } @book{hardin_generalized_2007, title = {Generalized linear models and extensions}, isbn = {9781597180146}, publisher = {Stata Press}, author = {Hardin, James William and Hilbe, Joseph}, month = feb, year = {2007} } @book{zuur_beginners_2013, title = {A Beginner's Guide to {GLM} and {GLMM} with {R}: A Frequentist and {Bayesian} Perspective for Ecologists}, isbn = {978-0-9571741-3-9}, shorttitle = {A {Beginner}'s {Guide} to {GLM} and {GLMM} with {R}}, publisher = {Highland Statistics Ltd}, author = {Zuur, Alain F. and Hilbe, Joseph M. and Leno, Elena N.}, month = may, year = {2013} } @book{millar_maximum_2011, title = {Maximum Likelihood Estimation and Inference: With Examples in R, {SAS} and {ADMB}}, isbn = {9781119977711}, shorttitle = {Maximum Likelihood Estimation and Inference}, abstract = {This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free {ADMB} software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical {paradigm.Key} features: Provides an accessible introduction to pragmatic maximum likelihood {modelling.Covers} more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated {likelihood.Adopts} a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real {data.Presents} numerous examples and case studies across a wide range of applications including medicine, biology and {ecology.Features} applications from a range of disciplines, with implementation in R, {SAS} and/or {ADMB.Provides} all program code and software extensions on a supporting {website.Confines} supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters. This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.}, language = {en}, publisher = {John Wiley \& Sons}, author = {Millar, Russell B.}, month = jul, year = {2011}, keywords = {Computers / Mathematical \& Statistical Software, Mathematics / Probability \& Statistics / General} } glmmTMB/R/0000755000176200001440000000000013616054060011753 5ustar liggesusersglmmTMB/R/methods.R0000644000176200001440000014571513614324717013565 0ustar liggesusers##' Extract Fixed Effects ##' ##' Extract fixed effects from a fitted \code{glmmTMB} model. ##' @name fixef ##' @title Extract fixed-effects estimates ##' @aliases fixef fixef.glmmTMB ##' @docType methods ##' @param object any fitted model object from which fixed effects estimates can ##' be extracted. ##' @param \dots optional additional arguments. Currently none are used in any ##' methods. ##' @return an object of class \code{fixef.glmmTMB} comprising a list of components (\code{cond}, \code{zi}, \code{disp}), each containing a (possibly zero-length) numeric vector of coefficients ##' @keywords models ##' @details The print method for \code{fixef.glmmTMB} object \emph{only displays non-trivial components}: in particular, the dispersion parameter estimate is not printed for models with a single (intercept) dispersion parameter (see examples) ##' @examples ##' data(sleepstudy, package = "lme4") ##' fm1 <- glmmTMB(Reaction ~ Days, sleepstudy) ##' (f1 <- fixef(fm1)) ##' f1$cond ##' ## show full coefficients, including dispersion parameter ##' unlist(f1) ##' print.default(f1) ##' @importFrom nlme fixef ##' @export fixef ##' @export fixef.glmmTMB <- function(object, ...) { pl <- object$obj$env$parList(object$fit$par, object$fit$parfull) structure(list(cond = setNames(pl$beta, colnames(getME(object, "X"))), zi = setNames(pl$betazi, colnames(getME(object, "Xzi"))), disp = setNames(pl$betad, colnames(getME(object, "Xd")))), class = "fixef.glmmTMB") } ## general purpose matching between component names and printable names cNames <- list(cond = "Conditional model", zi = "Zero-inflation model", disp = "Dispersion model") ## check identity without worrying about environments etc. ident <- function(x,target) isTRUE(all.equal(x,target)) formComp <- function(object,type="dispformula",target) { ident(object$modelInfo$allForm[[type]],target) || ident(object$call[[type]],target) } ## FIXME: this is a bit ugly. On the other hand, a single-parameter ## dispersion model without a (... ?) trivialDisp <- function(object) { formComp(object,"dispformula",~1) } zeroDisp <- function(object) { formComp(object,"dispformula",~0) } ## no roxygen for now ... # @param xnm vector of fixed-effect parameter names (e.g. names(fixef(m)$disp)) # @param nm name of component (e.g. "disp") trivialFixef <- function(xnm,nm) { length(xnm)==0 || (nm %in% c('d','disp') && identical(xnm,'(Intercept)')) ## FIXME: inconsistent tagging; should change 'Xd' to 'Xdisp'? } ##' @method print fixef.glmmTMB ##' @export print.fixef.glmmTMB <- function(x, digits = max(3, getOption("digits") - 3), ...) { for(nm in names(x)) { if (!trivialFixef(names(x[[nm]]),nm)) { cat(sprintf("\n%s:\n", cNames[[nm]])) print.default(format(x[[nm]], digits=digits), print.gap = 2L, quote = FALSE) } } invisible(x) } ##' Extract Random Effects ##' ##' Extract random effects from a fitted \code{glmmTMB} model, both ##' for the conditional model and zero inflation. ##' ##' @param object a \code{glmmTMB} model. ##' @param condVar whether to include conditional variances in result. ##' @param \dots some methods for this generic function require additional ##' arguments. ##' ##' @return ##' \itemize{ ##' \item For \code{ranef}, an object of class \code{ranef.glmmTMB} with two components: ##' \describe{ ##' \item{cond}{a list of data frames, containing random effects ##' for the conditional model.} ##' \item{zi}{a list of data frames, containing random effects for ##' the zero inflation.} ##' } ##' If \code{condVar=TRUE}, the individual list elements within the ##' \code{cond} and \code{zi} components (corresponding to individual ##' random effects terms) will have associated \code{condVar} attributes ##' giving the conditional variances of the random effects values. ##' These are in the form of three-dimensional arrays: see ##' \code{\link{ranef.merMod}} for details. The only difference between ##' the packages is that the attributes are called \sQuote{postVar} ##' in \pkg{lme4}, vs. \sQuote{condVar} in \pkg{glmmTMB}. ##' \item For \code{coef.glmmTMB}: a similar list, but containing ##' the overall coefficient value for each level, i.e., the sum of ##' the fixed effect estimate and the random effect value for that ##' level. \emph{Conditional variances are not yet available as ##' an option for} \code{coef.glmmTMB}. ##' \item For \code{as.data.frame}: a data frame with components ##' \describe{ ##' \item{component}{part of the model to which the random effects apply (conditional or zero-inflation)} ##' \item{grpvar}{grouping variable} ##' \item{term}{random-effects term (e.g., intercept or slope)} ##' \item{grp}{group, or level of the grouping variable} ##' \item{condval}{value of the conditional mode} ##' \item{condsd}{conditional standard deviation} ##' } ##' } ##' ##' @note When a model has no zero inflation, the ##' \code{ranef} and \code{coef} print methods simplify the ##' structure shown, by default. To show the full list structure, use ##' \code{print(ranef(model),simplify=FALSE)} or the analogous ##' code for \code{coef}. ##' In all cases, the full list structure is used to access ##' the data frames, see example. ##' ##' @seealso \code{\link{fixef.glmmTMB}}. ##' ##' @examples ##' if (requireNamespace("lme4")) { ##' data(sleepstudy, package="lme4") ##' model <- glmmTMB(Reaction ~ Days + (1|Subject), sleepstudy) ##' rr <- ranef(model) ##' print(rr, simplify=FALSE) ##' ## extract Subject conditional modes for conditional model ##' rr$cond$Subject ##' as.data.frame(rr) ##' } ##' @aliases ranef ranef.glmmTMB ##' @importFrom nlme ranef ##' @export ranef ##' @export ranef.glmmTMB <- function(object, condVar=TRUE, ...) { ## The arrange() function converts a vector of random effects to a list of ## data frames, in the same way as lme4 does. ## FIXME: add condVar, make sure format matches lme4 arrange <- function(x, sd, listname) { cnms <- object$modelInfo$reTrms[[listname]]$cnms flist <- object$modelInfo$reTrms[[listname]]$flist if (!is.null(cnms)) { levs <- lapply(fl <- flist, levels) asgn <- attr(fl, "assign") nc <- vapply(cnms, length, 1L) ## number of columns (terms) per RE nb <- nc * vapply(levs, length, 1L)[asgn] ## number of elements per RE nbseq <- rep.int(seq_along(nb), nb) ## splitting vector ml <- split(x, nbseq) for (i in seq_along(ml)) { ml[[i]] <- matrix(ml[[i]], ncol=nc[i], byrow=TRUE, dimnames=list(NULL, cnms[[i]])) } if (!is.null(sd)) { sd <- split(sd,nbseq) for (i in seq_along(sd)) { ii <- asgn[i] nr <- length(levs[[ii]]) a <- array(NA,dim=c(nc[i],nc[i],nr)) ## fill in diagonals: off-diagonals will stay NA (!) ## unless we bother to retrieve conditional covariance info ## from the fit ## when nc>1, what order is the sd vector in? ## guessing, level-wise for (j in seq(nr)) { a[cbind(seq(nc[i]),seq(nc[i]),j)] <- (sd[[i]][nc[i]*(j-1)+seq(nc[i])])^2 } sd[[i]] <- a } } ## combine RE matrices from all terms with the same grouping factor x <- lapply(seq_along(fl), function(i) { d <- data.frame(do.call(cbind, ml[asgn==i]), row.names=levs[[i]], check.names=FALSE) if (!is.null(sd)) { ## attach conditional variance info ## called "condVar", *not* "postVar" (contrast to lme4) attr(d, "condVar") <- if (length(w <- which(asgn==i))>1) { ## FIXME: set names? sd[w] ## if more than one term, list } else sd[[w]] ## else just the array } return(d) }) names(x) <- names(fl) return(x) } ## if !is.null(cnms) else { list() } } ## arrange() pl <- getParList(object) ## see VarCorr.R if (condVar && hasRandom(object)) { ss <- summary(object$sdr,"random") sdl <- list(b=ss[rownames(ss)=="b","Std. Error"], bzi=ss[rownames(ss)=="bzi","Std. Error"]) } else sdl <- NULL structure(list(cond = arrange(pl$b, sdl$b, "cond"), zi = arrange(pl$bzi, sdl$bzi, "zi")), class = "ranef.glmmTMB") } ##' @method print ranef.glmmTMB ##' @export print.ranef.glmmTMB <- function(x, simplify=TRUE, ...) { print(if (simplify && length(x$zi) == 0L) unclass(x$cond) else unclass(x), ...) invisible(x) } ##' @method print coef.glmmTMB ##' @export print.coef.glmmTMB <- print.ranef.glmmTMB ##' Extract or Get Generalize Components from a Fitted Mixed Effects Model ##' ##' @aliases getME ##' @param object a fitted \code{glmmTMB} object ##' @param name of the component to be retrieved ##' @param \dots ignored, for method compatibility ##' ##' @seealso \code{\link[lme4]{getME}} ##' Get generic and re-export: ##' @importFrom lme4 getME ##' @export getME ##' ##' @method getME glmmTMB ##' @export getME.glmmTMB <- function(object, name = c("X", "Xzi","Z", "Zzi", "Xd", "theta", "beta"), ...) { if(missing(name)) stop("'name' must not be missing") ## Deal with multiple names -- "FIXME" is inefficiently redoing things if (length(name <- as.character(name)) > 1) { names(name) <- name return(lapply(name, getME, object = object)) } if(name == "ALL") ## recursively get all provided components return(sapply(eval(formals()$name), getME.glmmTMB, object=object, simplify=FALSE)) stopifnot(inherits(object, "glmmTMB")) name <- match.arg(name) oo.env <- object$obj$env ### Start of the switch allpars <- oo.env$parList(object$fit$par, object$fit$parfull) switch(name, "X" = oo.env$data$X, "Xzi" = oo.env$data$Xzi, "Z" = oo.env$data$Z, "Zzi" = oo.env$data$Zzi, "Xd" = oo.env$data$Xd, "theta" = allpars$theta , "beta" = unlist(allpars[c("beta","betazi","betad")]), "..foo.." = # placeholder! stop(gettextf("'%s' is not implemented yet", sprintf("getME(*, \"%s\")", name))), ## otherwise stop(sprintf("Mixed-Effects extraction of '%s' is not available for class \"%s\"", name, class(object)))) }## {getME} ## FIXME: (1) why is this non-standard (containing nobs, nall?) ## (2) do we really need to document it?? ## Extract the log likelihood of a glmmTMB model ## ## @return object of class \code{logLik} with attributes ## \item{val}{log likelihood} ## \item{nobs,nall}{number of non NA observations initially supplied to TMB} ## \item{df}{number of parameters} ##' @importFrom stats logLik ##' @export logLik.glmmTMB <- function(object, ...) { if(!is.null(object$sdr)){ val <- if(object$sdr$pdHess){-object$fit$objective}else{NA} }else val <- -object$fit$objective nobs <- nobs.glmmTMB(object) df <- sum( ! names(object$fit$parfull) %in% c("b", "bzi") ) structure(val, nobs = nobs, nall = nobs, df = df, class = "logLik") } ##' @importFrom stats nobs ##' @export nobs.glmmTMB <- function(object, ...) sum(!is.na(object$obj$env$data$yobs)) ##' @importFrom stats df.residual ##' @method df.residual glmmTMB ##' @export ## TODO: not clear whether the residual df should be based ## on p=length(beta) or p=length(c(theta,beta)) ... but ## this is just to allow things like aods3::gof to work ... ## Taken from LME4, including the todo ## df.residual.glmmTMB <- function(object, ...) { nobs(object)-length(object$fit$par) } ##' Calculate Variance-Covariance Matrix for a Fitted glmmTMB model ##' ##' @param object a \dQuote{glmmTMB} fit ##' @param full return a full variance-covariance matrix? ##' @param \dots ignored, for method compatibility ##' @return By default (\code{full==FALSE}), a list of separate variance-covariance matrices for each model component (conditional, zero-inflation, dispersion). If \code{full==TRUE}, a single square variance-covariance matrix for \emph{all} top-level model parameters (conditional, dispersion, and variance-covariance parameters) ##' @importFrom TMB MakeADFun sdreport ##' @importFrom stats vcov ##' @export vcov.glmmTMB <- function(object, full=FALSE, ...) { REML <- isREML(object) if(is.null(sdr <- object$sdr)) { warning("Calculating sdreport. Use se=TRUE in glmmTMB to avoid repetitive calculation of sdreport") sdr <- sdreport(object$obj, getJointPrecision=REML) } if (REML) { ## NOTE: This code would also work in non-REML case provided ## that jointPrecision is present in the object. Q <- sdr$jointPrecision whichNotRandom <- which( ! rownames(Q) %in% c("b", "bzi") ) Qm <- GMRFmarginal(Q, whichNotRandom) cov.all.parms <- solve(as.matrix(Qm)) } else { cov.all.parms <- sdr$cov.fixed } keepTag <- if (full) { "." } else if (!trivialDisp(object)) { "beta*" } else "beta($|[^d])" to_keep <- grep(keepTag,colnames(cov.all.parms)) # only keep betas covF <- cov.all.parms[to_keep,to_keep,drop=FALSE] mkNames <- function(tag) { X <- getME(object,paste0("X",tag)) if (trivialFixef(nn <- colnames(X),tag) ## if 'full', keep disp even if trivial, if used by family && !(full && tag =="d" && (usesDispersion(family(object)$family) && !zeroDisp(object)))) { return(character(0)) } return(paste(tag,nn,sep="~")) } nameList <- setNames(list(colnames(getME(object,"X")), mkNames("zi"), mkNames("d")), names(cNames)) if(full) { ## FIXME: haven't really decided if we should drop the ## trivial variance-covariance dispersion parameter ?? ## if (trivialDisp(object)) ## res <- covF[-nrow(covF),-nrow(covF)] reNames <- function(tag) { re <- object$modelInfo$reStruc[[paste0(tag,"ReStruc")]] nn <- mapply(function(n,L) paste(n,seq(L),sep="."), names(re), sapply(re,"[[","blockNumTheta")) if (length(nn)==0) return(nn) return(paste("theta",gsub(" ","",nn),sep="_")) } nameList <- c(nameList,list(theta=reNames("cond"),thetazi=reNames("zi"))) } ## drop NA-mapped variables ## for matching map names vs nameList components ... par_components <- c("beta","betazi","betad","theta","thetazi") map <- object$obj$env$map for (m in seq_along(map)) { if (length(NAmap <- which(is.na(map[[m]])))>0) { w <- match(names(map)[m],par_components) ## if (length(nameList)>=w) { ## may not exist if !full nameList[[w]] <- nameList[[w]][-NAmap] } } } if (full) { colnames(covF) <- rownames(covF) <- unlist(nameList) res <- covF ## return just a matrix in this case } else { splitMat <- function(x) { ss <- split(seq_along(colnames(x)), colnames(x)) lapply(ss,function(z) x[z,z,drop=FALSE]) } covList <- splitMat(covF) names(covList) <- names(cNames)[match(names(covList),c("beta","betazi","betad"))] for (nm in names(covList)) { if (length(xnms <- nameList[[nm]])==0) { covList[[nm]] <- NULL } else dimnames(covList[[nm]]) <- list(xnms,xnms) } res <- covList ## FIXME: should vcov always return a three-element list ## (with NULL values for trivial models)? class(res) <- c("vcov.glmmTMB","matrix") } return(res) } ##' @method print vcov.glmmTMB ##' @export print.vcov.glmmTMB <- function(x,...) { for (nm in names(x)) { cat(cNames[[nm]],":\n",sep="") print(x[[nm]]) cat("\n") } invisible(x) } cat.f <- function(...) cat(..., fill = TRUE) .prt.call.glmmTMB <- function(call, long = TRUE) { pass <- 0 if (!is.null(cc <- call$formula)){ cat.f("Formula: ", deparse(cc)) rhs <- cc[[2]] if (!is.null(rhs)) { pass<-nchar(deparse(rhs)) } } if(!identical(cc <- deparse(call$ziformula),"~0")) cat.f("Zero inflation: ",rep(' ',pass+2), cc, sep='') if(!identical(cc <- deparse(call$dispformula),"~1")) cat.f("Dispersion: ",rep(' ',pass+2), cc, sep='') if (!is.null(cc <- call$data)) cat.f("Data:", deparse(cc)) if (!is.null(cc <- call$weights)) cat.f("Weights:", deparse(cc)) if (!is.null(cc <- call$offset)) cat.f(" Offset:", deparse(cc)) # if (long && length(cc <- call$control) && # !identical((dc <- deparse(cc)), "lmerControl()")) ## && !identical(eval(cc), lmerControl())) # cat.f("Control:", dc) # if (!is.null(cc <- call$subset)) # cat.f(" Subset:", deparse(cc)) } ### FIXME: attempted refactoring ... cat.f2 <- function(call,component,label,lwid,fwid=NULL,cind=NULL) { if (!is.null(cc <- call[[component]])) { if (!is.null(cind)) { ## try to extract component (of formula) if (!is.null(ccc <- cc[[cind]])) cc <- ccc } f1 <- format(paste0(label,":"),width=lwid,justify="right") f2 <- deparse(cc) if (!is.null(fwid)) { f2 <- format(f2,width=fwid,justify="right") } cat(f1,f2,fill=TRUE) } } ## reworked version .prt.call.glmmTMB2 <- function(call, long = TRUE) { labs <- c("Formula","Zero inflation","Dispersion","Data", "Weights","Offset","Control","Subset") components <- c("formula","ziformula","dispformula", "data","weights","offset","control","subset") lwid1 <- max(nchar(labs[1:3]))+2 for (i in 1:3) { cat.f2(call,components[i],labs[i],lwid1,cind=2) } lwid2 <- max(nchar(labs[-(1:3)]))+1 for (i in 4:6) { cat.f2(call,components[i],labs[i],lwid2) } if (long && length(cc <- call$control) && (deparse(cc) != "lmerControl()")) cat.f2(call,"Control","control",lwid2) cat.f2(call,"Subset","subset",lwid2) } ## following https://github.com/glmmTMB/glmmTMB/issues/134#issuecomment-160805926 ## don't use ##' until we're ready to generate a man page ## @param ff name of family (character) ## @param s dispersion (results of sigma(x) for original object printDispersion <- function(ff,s) { ## dispersion if (usesDispersion(ff)) { if (ff %in% .classicDispersionFamilies) { dname <- "Dispersion estimate" sname <- "sigma^2" sval <- s^2 } else { dname <- "Overdispersion parameter" sname <- "" sval <- s } cat(sprintf("\n%s for %s family (%s): %s", dname,ff,sname, formatC(sval,digits=3)),"\n") } NULL } .tweedie_power <- function(object) { unname(plogis(object$fit$par["thetaf"]) + 1) } ## Print family specific parameters ## @param ff name of family (character) ## @param object glmmTMB output #' @importFrom stats plogis printFamily <- function(ff, object) { if (ff == "tweedie") { power <- .tweedie_power(object) cat(sprintf("\nTweedie power parameter: %s", formatC(power, digits=3)), "\n") } NULL } ##' @importFrom lme4 .prt.aictab ##' @method print glmmTMB ##' @export print.glmmTMB <- function(x, digits = max(3, getOption("digits") - 3), correlation = NULL, symbolic.cor = FALSE, signif.stars = getOption("show.signif.stars"), longCall = TRUE, ranef.comp = "Std.Dev.", ...) { ## Type Of Model fit --- REML? ---['class'] & Family & Call .prt.call.glmmTMB(x$call, long=longCall) ## the 'digits' argument should have an action here aictab <- c(AIC = AIC(x), BIC = BIC(x), logLik = logLik(x), df.resid = df.residual(x)) .prt.aictab(aictab, digits=digits+1) ## varcorr if (!all(sapply(vc <- VarCorr(x),is.null))) { cat("Random-effects (co)variances:\n") print(VarCorr(x), digits=digits, comp = ranef.comp) } ## ngroups gvec <- list(obs=sprintf("\nNumber of obs: %d",nobs(x))) ng <- ngrps.glmmTMB(x) for (i in seq_along(ng)) { if (length(ng[[i]])>0) { nm <- names(ng)[i] gvec[[nm]] <- paste0(cNames[nm],": ", paste(paste(names(ng[[i]]), ng[[i]], sep=", "), collapse="; ")) } } cat(do.call(paste,c(gvec,list(sep=" / "))),fill=TRUE) if(trivialDisp(x)) {# if trivial print here, else below(~x) or none(~0) printDispersion(x$modelInfo$family$family,sigma(x)) } ## Family specific parameters printFamily(x$modelInfo$family$family, x) ## Fixed effects: if(length(cf <- fixef(x)) > 0) { cat("\nFixed Effects:\n") print(cf, ...) } else cat("No fixed effect coefficients\n") invisible(x) } ##' @export model.frame.glmmTMB <- function(formula, ...) { formula$frame } ##' Compute residuals for a glmmTMB object ##' ##' @param object a \dQuote{glmmTMB} object ##' @param type (character) residual type ##' @param \dots ignored, for method compatibility ##' @importFrom stats fitted model.response residuals ##' @export residuals.glmmTMB <- function(object, type=c("response", "pearson"), ...) { type <- match.arg(type) if(type=="pearson" &((object$call$ziformula != ~0)|(object$call$dispformula != ~1))) { stop("pearson residuals are not implemented for models with zero-inflation or variable dispersion") } mr <- model.response(object$frame) wts <- model.weights(model.frame(object)) ## binomial model specified as (success,failure) if (!is.null(dim(mr))) { wts <- mr[,1]+mr[,2] mr <- mr[,1]/wts } else if (is.factor(mr)) { ## ?binomial: ## "‘success’ is interpreted as the factor not having the first level" nn <- names(mr) mr <- as.numeric(as.numeric(mr)>1) names(mr) <- nn ## restore stripped names } r <- mr - fitted(object) res <- switch(type, response=r, pearson={ if (is.null(v <- family(object)$variance)) stop("variance function undefined for family ", sQuote(family(object)$family),"; cannot compute", " Pearson residuals") vv <- switch(length(formals(v)), v(fitted(object)), v(fitted(object),sigma(object)), stop("variance function should take 1 or 2 arguments")) r <- r/sqrt(vv) if (!is.null(wts)) { r <- r*sqrt(wts) } r }) return(res) } ## Helper to get CI of simple *univariate monotone* parameter ## function, i.e. a function of 'fit$par' and/or 'fit$parfull'. ## Examples: 'sigma.glmmTMB' and some parts of 'VarCorr.glmmTMB'. ##' @importFrom stats qchisq .CI_univariate_monotone <- function(object, f, reduce=NULL, level=0.95, name.prepend=NULL, estimate = TRUE) { x <- object par <- x$fit$par i <- seq_along(x$fit$parfull) ## Pointers into long par vector r <- x$obj$env$random if(!is.null(r)) i <- i[-r] ## Pointers into short par subset sdr <- x$sdr sdpar <- summary(sdr, "fixed")[,2] q <- sqrt(qchisq(level, df=1)) ans <- list() x$fit$parfull[i] <- x$fit$par <- par - q * sdpar ans$lower <- f(x) x$fit$parfull[i] <- x$fit$par <- par + q * sdpar ans$upper <- f(x) if (estimate) { ans$Estimate <- f(object) } if(is.null(reduce)) reduce <- function(x) x ans <- lapply(ans, reduce) nm <- names(ans) tmp <- cbind(ans$lower, ans$upper) if (is.null(tmp) || nrow(tmp) == 0L) return (NULL) sort2 <- function(x) if(any(is.nan(x))) x * NaN else sort(x) ans <- cbind( t( apply(tmp, 1, sort2) ) , ans$Estimate ) colnames(ans) <- nm if (!is.null(name.prepend)) name.prepend <- rep(name.prepend, length.out = nrow(ans)) rownames(ans) <- paste(name.prepend, rownames(ans), sep="") ans } ## copied from 'stats' format.perc <- function (probs, digits) { paste(format(100 * probs, trim = TRUE, scientific = FALSE, digits = digits), "%") } ##' Calculate confidence intervals ##' ##' @details ##' Available methods are ##' \describe{ ##' \item{"wald"}{These intervals are based on the standard errors ##' calculated for parameters on the scale ##' of their internal parameterization depending on the family. Derived ##' quantities such as standard deviation parameters and dispersion ##' parameters are back-transformed. It follows that confidence ##' intervals for these derived quantities are typically asymmetric.} ##' \item{"profile"}{This method computes a likelihood profile ##' for the specified parameter(s) using \code{profile.glmmTMB}; ##' fits a spline function to each half of the profile; and ##' inverts the function to find the specified confidence interval.} ##' \item{"uniroot"}{This method uses the \code{\link{uniroot}} ##' function to find critical values of one-dimensional profile ##' functions for each specified parameter.} ##' } ##' At present, "wald" returns confidence intervals for variance ##' parameters on the standard deviation/correlation scale, while ##' "profile" and "uniroot" report them on the underlying ("theta") ##' scale: for each random effect, the first set of parameter values ##' are standard deviations on the log scale, while remaining parameters ##' represent correlations on the scaled Cholesky scale (see the ##' ##' ##' @importFrom stats qnorm confint ##' @export ##' @param object \code{glmmTMB} fitted object. ##' @param parm which parameters to profile, specified #' \itemize{ #' \item by index (position) [\emph{after} component selection for \code{confint}, if any] #' \item by name (matching the row/column names of \code{vcov(object,full=TRUE)}) #' \item as \code{"theta_"} (random-effects variance-covariance parameters), \code{"beta_"} (conditional and zero-inflation parameters), or \code{"disp_"} or \code{"sigma"} (dispersion parameters) #' } #' Parameter indexing by number may give unusual results when #' some parameters have been fixed using the \code{map} argument: #' please report surprises to the package maintainers. ##' @param level Confidence level. ##' @param method 'wald', 'profile', or 'uniroot': see Details ##' function) ##' @param component Which of the three components 'cond', 'zi' or ##' 'other' to select. Default is to select 'all'. ##' @param estimate (logical) add a third column with estimate ? ##' @param parallel method (if any) for parallel computation ##' @param ncpus number of CPUs/cores to use for parallel computation ##' @param cl cluster to use for parallel computation ##' @param full CIs for all parameters (including dispersion) ? ##' @param ... arguments may be passed to \code{\link{profile.merMod}} or ##' \code{\link[TMB]{tmbroot}} ##' @examples ##' data(sleepstudy, package="lme4") ##' model <- glmmTMB(Reaction ~ Days + (1|Subject), sleepstudy) ##' model2 <- glmmTMB(Reaction ~ Days + (1|Subject), sleepstudy, ##' dispformula= ~I(Days>8)) ##' confint(model) ## Wald/delta-method CIs ##' confint(model,parm="theta_") ## Wald/delta-method CIs ##' confint(model,parm=1,method="profile") confint.glmmTMB <- function (object, parm = NULL, level = 0.95, method=c("wald", "Wald", "profile", "uniroot"), component = c("all", "cond", "zi", "other"), estimate = TRUE, parallel = c("no", "multicore", "snow"), ncpus = getOption("profile.ncpus", 1L), cl = NULL, full = FALSE, ...) { method <- tolower(match.arg(method)) if (method=="wald") { dots <- list(...) if (length(dots)>0) { if (is.null(names(dots))) { warning("extra (unnamed) arguments ignored") } else { warning(paste("extra arguments ignored: ", paste(names(dots),collapse=", "))) } } } components <- match.arg(component, several.ok = TRUE) components.has <- function(x) any(match(c(x, "all"), components, nomatch=0L)) > 0L a <- (1 - level)/2 a <- c(a, 1 - a) pct <- format.perc(a, 3) fac <- qnorm(a) estimate <- as.logical(estimate) ci <- matrix(NA, nrow=0, ncol=2 + estimate, dimnames=list(NULL, if (!estimate) pct else c(pct, "Estimate"))) if (!is.null(parm) || method!="wald") { parm <- getParms(parm, object, full) } if (method=="wald") { map <- object$modelInfo$map for (component in c("cond", "zi") ) { if (components.has(component) && (nbeta <- length(fixef(object)[[component]]))>0) { ## variance and estimates vv <- vcov(object)[[component]] cf <- fixef(object)[[component]] ## strip tag (only really necessary for zi~ nn <- gsub(paste0(component,"~"),"",colnames(vv)) ## vcov only includes estimated (not mapped/fixed) ## fixed-effect parameters cf <- cf[nn] ss <- diag(vv) ## using [[-extraction; need to add component name explicitly if (length(cf)>0) { names(cf) <- names(ss) <- paste(component, names(cf), sep=".") ses <- sqrt(ss) ci.tmp <- cf + ses %o% fac if (estimate) ci.tmp <- cbind(ci.tmp, cf) ci <- rbind(ci, ci.tmp) } ## VarCorr -> stddev cfun <- function(x) { ss <- attr(x, "stddev") names(ss) <- paste(component,"Std.Dev",names(ss),sep=".") cc <- attr(x,"correlation") if (length(cc)>1) { nn <- outer(colnames(cc),rownames(cc),paste,sep=".") cc <- cc[lower.tri(cc)] nn <- paste(component,"Cor",nn[lower.tri(nn)],sep=".") names(cc) <- nn ss <- c(ss,cc) } return(ss) } reduce <- function(VC) sapply(VC[[component]], cfun) ci.sd <- .CI_univariate_monotone(object, VarCorr, reduce = reduce, level = level, ## name.prepend=paste(component, ## "Std.Dev.", ## sep="."), estimate = estimate) ## would consider excluding mapped parameters here ## (works automatically for fixed effects via vcov) ## but tough because of theta <-> sd/corr mapping; ## instead, eliminate rows below where lowerCI==upperCI ci <- rbind(ci, ci.sd) } } ## cond and zi components if (components.has("other")) { ## sigma ff <- object$modelInfo$family$family if (usesDispersion(ff)) { ci.sigma <- .CI_univariate_monotone(object, sigma, reduce = NULL, level=level, name.prepend="sigma", estimate = estimate) ci <- rbind(ci, ci.sigma) } ## Tweedie power if (ff == "tweedie") { ci.power <- .CI_univariate_monotone(object, .tweedie_power, reduce = NULL, level=level, name.prepend="Tweedie.power", estimate = estimate) ci <- rbind(ci, ci.power) } ## tweedie } ## model has 'other' component ## Take subset ## drop mapped values (where lower == upper) ci <- ci[ci[,2]!=ci[,1], , drop=FALSE] ## now get selected parameters if (!is.null(parm)) { ci <- ci[parm, , drop=FALSE] } else { ## drop residual std dev/trivial dispersion parameter if (!full) { ci <- ci[rownames(ci)!="sigma",, drop=FALSE] } } ## end Wald method } else if (method=="uniroot") { if (isREML(object)) stop("can't compute profiles for REML models at the moment (sorry)") ## FIXME: allow greater flexibility in specifying different ## ranges, etc. for different parameters plist <- parallel_default(parallel,ncpus) parallel <- plist$parallel do_parallel <- plist$do_parallel FUN <- function(n) { TMB::tmbroot(obj=object$obj, name=n, target=0.5*qchisq(level,df=1), ...) } if (do_parallel) { if (parallel == "multicore") { L <- parallel::mclapply(parm, FUN, mc.cores = ncpus) } else if (parallel=="snow") { if (is.null(cl)) { ## start cluster new_cl <- TRUE cl <- parallel::makePSOCKcluster(rep("localhost", ncpus)) } ## run L <- parallel::clusterApply(cl, parm, FUN) if (new_cl) { ## stop cluster parallel::stopCluster(cl) } } } else { ## non-parallel L <- lapply(as.list(parm), FUN) } L <- do.call(rbind,L) rownames(L) <- rownames(vcov(object,full=TRUE))[parm] if (estimate) { ee <- object$obj$env par <- ee$last.par.best if (!is.null(ee$random)) par <- par[-ee$random] par <- par[parm] L <- cbind(L,par) } ci <- rbind(ci,L) ## really just adding column names! } else { ## profile CIs pp <- profile(object, parm=parm, level_max=level, parallel=parallel,ncpus=ncpus, ...) ci <- confint(pp) } ## if only conditional, strip component prefix if (all(substr(rownames(ci),1,5)=="cond.")) { rownames(ci) <- sub("^cond\\.","",rownames(ci)) } return(ci) } ##' @rdname glmmTMB_methods ##' @param x a fitted \code{glmmTMB} object ##' @export ## modified because e.g. "disp" component didn't get a $reTrms ## component (updated fitTMB to save a separate "terms" component) terms.glmmTMB <- function(x, component="cond", part="fixed", ...) { if (part != "fixed") stop("only fixed terms currently available") if ("terms" %in% names(x$modelInfo)) { tt <- x$modelInfo$terms[[component]] } else { ## allow back-compatibility tt <- x$modelInfo$reTrms[[component]]$terms } return(tt[[part]]) } ##' @export extractAIC.glmmTMB <- function(fit, scale, k = 2, ...) { L <- logLik(fit) edf <- attr(L,"df") return(c(edf,c(-2*L + k*edf))) } ## deparse(.) returning \bold{one} string ## copied from lme4/R/utilities.R ## Protects against the possibility that results from deparse() will be ## split after 'width.cutoff' (by default 60, maximally 500) safeDeparse <- function(x, collapse=" ") paste(deparse(x, 500L), collapse=collapse) abbrDeparse <- function(x, width=60) { r <- deparse(x, width) if(length(r) > 1) paste(r[1], "...") else r } ##' @importFrom methods is ##' @importFrom stats var getCall pchisq anova ##' @export anova.glmmTMB <- function (object, ..., model.names = NULL) { mCall <- match.call(expand.dots = TRUE) dots <- list(...) .sapply <- function(L, FUN, ...) unlist(lapply(L, FUN, ...)) ## detect multiple models, i.e. models in ... modp <- as.logical(vapply(dots, is, NA, "glmmTMB")) if (any(modp)) { mods <- c(list(object), dots[modp]) nobs.vec <- vapply(mods, nobs, 1L) if (var(nobs.vec) > 0) stop("models were not all fitted to the same size of dataset") if (is.null(mNms <- model.names)) mNms <- vapply(as.list(mCall)[c(FALSE, TRUE, modp)], safeDeparse, "") if (any(duplicated(mNms))) { warning("failed to find unique model names, assigning generic names") mNms <- paste0("MODEL", seq_along(mNms)) } if (length(mNms) != length(mods)) stop("model names vector and model list have different lengths") names(mods) <- sub("@env$", "", mNms) llks <- lapply(mods, logLik) ii <- order(Df <- vapply(llks, attr, FUN.VALUE = numeric(1), "df")) mods <- mods[ii] llks <- llks[ii] Df <- Df[ii] calls <- lapply(mods, getCall) data <- lapply(calls, `[[`, "data") if (!all(vapply(data, identical, NA, data[[1]]))) stop("all models must be fit to the same data object") header <- paste("Data:", abbrDeparse(data[[1]])) subset <- lapply(calls, `[[`, "subset") if (!all(vapply(subset, identical, NA, subset[[1]]))) stop("all models must use the same subset") if (!is.null(subset[[1]])) header <- c(header, paste("Subset:", abbrDeparse(subset[[1]]))) llk <- unlist(llks) chisq <- 2 * pmax(0, c(NA, diff(llk))) dfChisq <- c(NA, diff(Df)) val <- data.frame(Df = Df, AIC = .sapply(llks, AIC), BIC = .sapply(llks, BIC), logLik = llk, deviance = -2 * llk, Chisq = chisq, `Chi Df` = dfChisq, `Pr(>Chisq)` = pchisq(chisq, dfChisq, lower.tail = FALSE), row.names = names(mods), check.names = FALSE) class(val) <- c("anova", class(val)) forms <- lapply(lapply(calls, `[[`, "formula"), deparse) ziforms <- lapply(lapply(calls, `[[`, "ziformula"), deparse) dispforms <- lapply(lapply(calls, `[[`, "dispformula"), deparse) #FIXME only output nontrivial ziforms and dispforms structure(val, heading = c(header, "Models:", paste(paste(paste(rep(names(mods), times = lengths(forms)), unlist(forms), sep = ": "), unlist(ziforms), sep=", zi="), unlist(dispforms), sep=", disp="))) } else stop("no single-model anova() method for glmmTMB") } #' @importFrom stats predict #' @export fitted.glmmTMB <- function(object, ...) { predict(object,type="response") } .noSimFamilies <- NULL noSim <- function(x) { !is.na(match(x, .noSimFamilies)) } ##' Simulate from a glmmTMB fitted model ##' @method simulate glmmTMB ##' @param object glmmTMB fitted model ##' @param nsim number of response lists to simulate. Defaults to 1. ##' @param seed random number seed ##' @param ... extra arguments ##' @details Random effects are also simulated from their estimated distribution. ##' Currently, it is not possible to condition on estimated random effects. ##' @return returns a list of vectors. The list has length \code{nsim}. ##' Each simulated vector of observations is the same size as the vector of response variables in the original data set. ##' In the binomial family case each simulation is a two-column matrix with success/failure. ##' @importFrom stats simulate ##' @export simulate.glmmTMB<-function(object, nsim=1, seed=NULL, ...){ if(noSim(object$modelInfo$family$family)) { stop("Simulation code has not been implemented for this family") } ## copied from stats::simulate.lm if (!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) runif(1) if (is.null(seed)) RNGstate <- get(".Random.seed", envir = .GlobalEnv) else { R.seed <- get(".Random.seed", envir = .GlobalEnv) set.seed(seed) RNGstate <- structure(seed, kind = as.list(RNGkind())) on.exit(assign(".Random.seed", R.seed, envir = .GlobalEnv)) } family <- object$modelInfo$family$family ret <- replicate(nsim, object$obj$simulate(par = object$fit$parfull)$yobs, simplify=FALSE) if ( binomialType(family) ) { size <- object$obj$env$data$size ret <- lapply(ret, function(x) cbind(x, size - x, deparse.level=0) ) class(ret) <- "data.frame" rownames(ret) <- as.character(seq_len(nrow(ret[[1]]))) } else { ret <- as.data.frame(ret) } names(ret) <- paste0("sim_", seq_len(nsim)) attr(ret, "seed") <- RNGstate ret } #' Extract the formula of a glmmTMB object #' #' @param x a \code{glmmTMB} object #' @param component formula for which component of the model to return (conditional, zero-inflation, or dispersion) #' @param fixed.only (logical) drop random effects, returning only the fixed-effect component of the formula? #' @param ... unused, for generic consistency #' @importFrom lme4 nobars #' @export formula.glmmTMB <- function(x, fixed.only=FALSE, component=c("cond", "zi", "disp"), ...) { if (!fixed.only && missing(component)) { ## stats::formula.default extracts formula from call return(NextMethod(x, ...)) } component <- match.arg(component) af <- x$modelInfo$allForm ff <- if (component=="cond") af[["formula"]] else af[[paste0(component,"formula")]] if (fixed.only) { ff <- lme4::nobars(ff) } return(ff) } ## need this so we can get contrasts carried through properly to model.matrix #' Methods for extracting developer-level information from \code{glmmTMB} models #' @rdname glmmTMB_methods #' @param object a fitted \code{glmmTMB} object #' @param component model component ("cond", "zi", or "disp"; not all models contain all components) #' @param part whether to return results for the fixed or random effect part of the model (at present only \code{part="fixed"} is implemented for most methods) #' @param \dots additional arguments (ignored or passed to \code{\link{model.frame}}) #' @export model.matrix.glmmTMB <- function (object, component="cond", part="fixed", ...) { ## FIXME: model.matrix.lm has this stuff -- what does it do/do we want it? ## if (n_match <- match("x", names(object), 0L)) ## object[[n_match]] ## else { ## data <- model.frame(object, xlev = object$xlevels, ...) ## was calling NextMethod() on the model frame: failed after messing ## with terms structure ## could be more efficient to extract $X, $Z rather than re-building ## model matrix?? if (part != "fixed") stop("only fixed model matrices currently available") ff <- object$modelInfo$allForm form <- ff[[switch(component, cond="formula", zi="ziformula", disp="dispformula")]] model.matrix(lme4::nobars(form), model.frame(object, ...), contrasts.arg = object$modelInfo$contrasts) ## FIXME: what if contrasts are *different* for different components? (ugh) ## should at least write a test to flag this case ... } ## convert ranef object to a long-format data frame, e.g. suitable ## for ggplot2 (or homemade lattice plots) ## FIXME: have some gymnastics to do if terms, levels are different ## for different grouping variables - want to maintain ordering ## but still allow rbind()ing ##' @export ##' @rdname ranef.glmmTMB ##' @param x a \code{ranef.glmmTMB} object (i.e., the result of running \code{ranef} on a fitted \code{glmmTMB} model) ##' @param stringsAsFactors see \code{\link{data.frame}} as.data.frame.ranef.glmmTMB <- function(x, ..., stringsAsFactors = default.stringsAsFactors()) { tmpf <- function(x) do.call(rbind,lapply(names(x),asDf0,x=x,id=TRUE)) x0 <- lapply(x,tmpf) x1 <- Map(function(x,n) { if (!is.null(x)) x$component <- n; x }, x0, names(x)) xD <- do.call(rbind,x1) ## rename ... oldnames <- c("values","ind",".nn","se","id","component") newnames <- c("condval","term","grp","condsd","grpvar","component") names(xD) <- newnames[match(names(xD),oldnames)] ## reorder ... neworder <- c("component","grpvar","term","grp","condval") if ("condsd" %in% names(xD)) neworder <- c(neworder,"condsd") return(xD[neworder]) } #' @rdname bootmer_methods #' @title support methods for parametric bootstrapping #' @param object a fitted glmmTMB object #' @param newresp a new response vector #' @export #' @importFrom lme4 isLMM #' @importFrom lme4 refit ## don't export refit ... #' @description see \code{\link[lme4]{refit}} and \code{\link[lme4]{isLMM}} for details isLMM.glmmTMB <- function(object) { fam <- family(object) fam$family=="gaussian" && fam$link=="identity" } #' @export #' @rdname bootmer_methods #' @importFrom stats formula #' @param ... additional arguments (for generic consistency; ignored) #' @examples #' if (requireNamespace("lme4")) { #' \dontrun{ #' fm1 <- glmmTMB(count~mined+(1|spp), #' ziformula=~mined, #' data=Salamanders, #' family=nbinom1) #' b1 <- lme4::bootMer(fm1, FUN=function(x) fixef(x)$zi, nsim=20, .progress="txt") #' if (requireNamespace("boot")) { #' boot.ci(b1,type="perc") #' } #' } #' } #' @details #' These methods are still somewhat experimental (check your results carefully!), but they should allow parametric bootstrapping. They work by copying and replacing the original response column in the data frame passed to \code{glmmTMB}, so they will only work properly if (1) the data frame is still available in the environment and (2) the response variable is specified as a single symbol (e.g. \code{proportion} or a two-column matrix constructed on the fly with \code{cbind()}. Untested with binomial models where the response is specified as a factor. #' refit.glmmTMB <- function(object, newresp, ...) { cc <- getCall(object) newdata <- eval.parent(cc$data) if (is.null(newdata)) stop("can't locate original 'data' value") fresp <- formula(object)[[2]] mf0 <- model.frame(object) rcol <- attr(attr(mf0, "terms"), "response") rnm <- deparse(fresp) if (binomialType(family(object)$family)) { ## FIXME: check for factor column? if ("(weights)" %in% names(mf0)) { if (!rnm %in% names(newdata)) stop("can't find response in data") w <- rowSums(newresp) newdata[[rnm]] <- newresp[,1]/w newdata[["(weights)"]] <- w } else if (is.matrix(mf0[[rnm]])) { if (is.symbol(fresp)) { if (!rnm %in% names(newdata)) stop("can't find response in data") newdata[[rnm]] <- newresp } ## matrix response else if (identical(quote(cbind),fresp[[1]])) { rnm1 <- deparse(fresp[[2]]) rnm2 <- deparse(fresp[[3]]) if (!all(c(rnm1,rnm2) %in% names(newdata))) stop("can't find response in data") newdata[[rnm1]] <- newresp[,1] newdata[[rnm2]] <- newresp[,2] } else { stop("can't handle this data format, sorry ...") } } else { if (is.matrix(newresp)) newresp <- newresp[,1] newdata[[deparse(fresp)]] <- newresp } } else { newdata[[deparse(fresp)]] <- newresp } cc$data <- quote(newdata) return(eval(cc)) } ## copied from lme4, with addition of 'component' argument ## FIXME: migrate back to lme4? component is NULL for back-compat. ## FIXME: ## coef() method for all kinds of "mer", "*merMod", ... objects ## ------ should work with fixef() + ranef() alone coefMer <- function(object, component=NULL, ...) { if (length(list(...))) warning('arguments named "', paste(names(list(...)), collapse = ", "), '" ignored') fef <- fixef(object) if (!is.null(component)) fef <- fef[[component]] fef <- data.frame(rbind(fef), check.names = FALSE) ref <- ranef(object) if (!is.null(component)) ref <- ref[[component]] ## check for variables in RE but missing from FE, fill in zeros in FE accordingly refnames <- unlist(lapply(ref,colnames)) nmiss <- length(missnames <- setdiff(refnames,names(fef))) if (nmiss > 0) { fillvars <- setNames(data.frame(rbind(rep(0,nmiss))),missnames) fef <- cbind(fillvars,fef) } val <- lapply(ref, function(x) fef[rep.int(1L, nrow(x)),,drop = FALSE]) for (i in seq(a = val)) { refi <- ref[[i]] row.names(val[[i]]) <- row.names(refi) nmsi <- colnames(refi) if (!all(nmsi %in% names(fef))) stop("unable to align random and fixed effects") for (nm in nmsi) val[[i]][[nm]] <- val[[i]][[nm]] + refi[,nm] } class(val) <- "coef.mer" val } ## {coefMer} #' @rdname ranef.glmmTMB #' @export coef.glmmTMB <- function(object, condVar=FALSE, ...) { model.has.component <- function(x) { !is.null(object$modelInfo$reTrms[[x]]$cnms) } get.coef <- function(x) { if (!model.has.component(x)) return(list()) return(coefMer(object, component=x)) } res <- list( cond = get.coef("cond"), zi = get.coef("zi") ) if (condVar) { stop("condVar not (yet) available for coefficients") sdr <- TMB::sdreport(object$obj, getJointPrecision=TRUE) v <- solve(sdr$jointPrecision) ## FIXME:: sort out variance calculation, using Z and X } class(res) <- "coef.glmmTMB" return(res) } ##' Extract weights from a glmmTMB object ##' ##' @details ##' At present only explicitly specified ##' \emph{prior weights} (i.e., weights specified ##' in the \code{weights} argument) can be extracted from a fitted model. ##' \itemize{ ##' \item Unlike other GLM-type models such as \code{\link{glm}} or ##' \code{\link[lme4]{glmer}}, \code{weights()} does not currently return ##' the total number of trials when binomial responses are specified ##' as a two-column matrix. ##' \item Since \code{glmmTMB} does not fit models via iteratively ##' weighted least squares, \code{working weights} (see \code{\link[stats]{weights.glm}}) are unavailable. ##' } ##' @importFrom stats model.frame ##' @importFrom stats weights ##' @param object a fitted \code{glmmTMB} object ##' @param type weights type ##' @param ... additional arguments (not used; for methods compatibility) ##' @export weights.glmmTMB <- function(object, type="prior", ...) { type <- match.arg(type) ## other types are *not* OK if (length(list(...)>0)) { warning("unused arguments ignored: ", paste(shQuote(names(list(...))),collapse=",")) } stats::model.frame(object)[["(weights)"]] } glmmTMB/R/utils.R0000644000176200001440000005677213614324717013266 0ustar liggesusers## backward compat (copied from lme4) if((Rv <- getRversion()) < "3.2.1") { lengths <- function (x, use.names = TRUE) vapply(x, length, 1L, USE.NAMES = use.names) } rm(Rv) ## generate a list with names equal to values namedList <- function (...) { L <- list(...) snm <- sapply(substitute(list(...)), deparse)[-1] if (is.null(nm <- names(L))) nm <- snm if (any(nonames <- nm == "")) nm[nonames] <- snm[nonames] setNames(L, nm) } RHSForm <- function(form,as.form=FALSE) { if (!as.form) return(form[[length(form)]]) if (length(form)==2) return(form) ## already RHS-only ## by operating on RHS in situ rather than making a new formula ## object, we avoid messing up existing attributes/environments etc. form[[2]] <- NULL ## assumes response is *first* variable (I think this is safe ...) if (length(vars <- attr(form,"variables"))>0) { attr(form,"variables") <- vars[-2] } if (is.null(attr(form,"response"))) { attr(form,"response") <- 0 } if (length(facs <- attr(form,"factors"))>0) { attr(form,"factors") <- facs[-1,] } return(form) } `RHSForm<-` <- function(formula,value) { formula[[length(formula)]] <- value formula } ## Random Effects formula only ## reOnly <- function(f,response=FALSE) { ## response <- if (response && length(f)==3) f[[2]] else NULL ## reformulate(paste0("(", vapply(findbars(f), safeDeparse, ""), ")"), ## response=response) ## } sumTerms <- function(termList) { Reduce(function(x,y) makeOp(x,y,op=quote(`+`)),termList) } ## better version -- operates on language objects (no deparse()) reOnly <- function(f,response=FALSE,bracket=TRUE) { ff <- f if (bracket) ff <- lapply(findbars(ff),makeOp,quote(`(`)) ## bracket-protect terms ff <- sumTerms(ff) if (response && length(f)==3) { form <- makeOp(f[[2]],ff,quote(`~`)) } else { form <- makeOp(ff,quote(`~`)) } return(form) } ## combine unary or binary operator + arguments (sugar for 'substitute') ## FIXME: would be nice to have multiple dispatch, so ## (arg,op) gave unary, (arg,arg,op) gave binary operator makeOp <- function(x,y,op=NULL) { if (is.null(op) || missing(y)) { ## unary if (is.null(op)) { substitute(OP(X),list(X=x,OP=y)) } else { substitute(OP(X),list(X=x,OP=op)) } } else substitute(OP(X,Y), list(X=x,OP=op,Y=y)) } ## combines the right-hand sides of two formulas, or a formula and a symbol ## @param f1 formula #1 ## @param f2 formula #2 ## @examples ## if (FALSE) { ## still being exported despite "keywords internal" ?? ## addForm0(y~x,~1) ## addForm0(~x,~y) ## } ## @keywords internal addForm0 <- function(f1,f2,naked=FALSE) { tilde <- as.symbol("~") if (!identical(head(f2),tilde)) { f2 <- makeOp(f2,tilde) } if (length(f2)==3) warning("discarding LHS of second argument") RHSForm(f1) <- makeOp(RHSForm(f1),RHSForm(f2),quote(`+`)) return(f1) } ##' Combine right-hand sides of an arbitrary number of formulas ##' @param ... arguments to pass through to \code{addForm0} ##' @rdname splitForm ##' @export addForm <- function(...) { Reduce(addForm0,list(...)) } addArgs <- function(argList) { Reduce(function(x,y) makeOp(x,y,op=quote(`+`)),argList) } ##' list of specials -- taken from enum.R findReTrmClasses <- function() { names(.valid_covstruct) } ## expandGrpVar(quote(x*y)) ## expandGrpVar(quote(x/y)) expandGrpVar <- function(f) { form <- as.formula(makeOp(f,quote(`~`))) mm <- terms(form) toLang <- function(x) parse(text=x)[[1]] lapply(attr(mm,"term.labels"), toLang) } ##' expand interactions/combinations of grouping variables ##' ##' Modeled after lme4:::expandSlash, by Doug Bates ##' @param bb a list of naked grouping variables, i.e. 1 | f ##' @examples ##' ff <- glmmTMB:::fbx(y~1+(x|f/g)) ##' glmmTMB:::expandAllGrpVar(ff) ##' glmmTMB:::expandAllGrpVar(quote(1|(f/g)/h)) ##' glmmTMB:::expandAllGrpVar(quote(1|f/g/h)) ##' glmmTMB:::expandAllGrpVar(quote(1|f*g)) ##' @importFrom utils head ##' @keywords internal expandAllGrpVar <- function(bb) { ## Return the list of '/'-separated terms if (!is.list(bb)) expandAllGrpVar(list(bb)) else { for (i in seq_along(bb)) { esfun <- function(x) { if (length(x)==1) return(x) if (length(x)==2) { ## unary operator such as diag(1|f/g) ## return diag(...) + diag(...) + ... return(lapply(esfun(x[[2]]), makeOp,y=head(x))) } if (length(x)==3) { ## binary operator if (x[[1]]==quote(`|`)) { return(lapply(expandGrpVar(x[[3]]), makeOp,x=x[[2]],op=quote(`|`))) } else { return(setNames(makeOp(esfun(x[[2]]),esfun(x[[3]]), op=x[[1]]),names(x))) } } } ## esfun def. return(unlist(lapply(bb,esfun))) } ## loop over bb } } ## sugar: this returns the operator, whether ~ or something else head.formula <- head.call <- function(x, ...) { x[[1]] } ## sugar: we can call head on a symbol and get back the symbol head.name <- function(x) { x } ##' (f)ind (b)ars e(x)tended: recursive ##' ##' @param term a formula or piece of a formula ##' @param debug (logical) debug? ##' @param specials list of special terms ##' @param default.special character: special to use for parenthesized terms - i.e. random effects terms with unspecified structure ##' 1. atom (not a call or an expression): NULL ##' 2. special, i.e. foo(...) where "foo" is in specials: return term ##' 3. parenthesized term: \emph{if} the head of the head is | (i.e. ##' it is of the form (xx|gg), then convert it to the default ##' special type; we won't allow pathological cases like ##' ((xx|gg)) ... [can we detect them?] ##' @examples ##' splitForm(quote(us(x,n=2))) ##' @keywords internal fbx <- function(term,debug=FALSE,specials=character(0), default.special="us") { ds <- eval(substitute(as.name(foo),list(foo=default.special))) if (is.name(term) || !is.language(term)) return(NULL) if (list(term[[1]]) %in% lapply(specials,as.name)) { if (debug) cat("special: ",deparse(term),"\n") return(term) } if (head(term) == as.name('|')) { ## found x | g if (debug) cat("bar term:",deparse(term),"\n") return(makeOp(term,ds)) } if (head(term) == as.name("(")) { ## found (...) if (debug) cat("paren term:",deparse(term),"\n") return(fbx(term[[2]],debug,specials)) } stopifnot(is.call(term)) if (length(term) == 2) { ## unary operator, decompose argument if (debug) cat("unary operator:",deparse(term[[2]]),"\n") return(fbx(term[[2]],debug,specials)) } ## binary operator, decompose both arguments if (debug) cat("binary operator:",deparse(term[[2]]),",", deparse(term[[3]]),"\n") c(fbx(term[[2]],debug,specials), fbx(term[[3]],debug,specials)) } ##' Parse a formula into fixed formula and random effect terms, ##' treating 'special' terms (of the form foo(x|g[,m])) appropriately ##' ##' Taken from Steve Walker's lme4ord, ##' ultimately from the flexLambda branch of lme4 ##' . Mostly for internal use. ##' @title Split formula containing special random effect terms ##' @param formula a formula containing special random effect terms ##' @param defaultTerm default type for non-special RE terms ##' @param allowFixedOnly (logical) are formulas with no RE terms OK? ##' @param allowNoSpecials (logical) are formulas with only standard RE terms OK? ##' @return a list containing elements \code{fixedFormula}; ##' \code{reTrmFormulas} list of \code{x | g} formulas for each term; ##' \code{reTrmAddArgs} list of function+additional arguments, i.e. \code{list()} (non-special), \code{foo()} (no additional arguments), \code{foo(addArgs)} (additional arguments); \code{reTrmClasses} (vector of special functions/classes, as character) ##' @examples ##' splitForm(~x+y) ## no specials or RE ##' splitForm(~x+y+(f|g)) ## no specials ##' splitForm(~x+y+diag(f|g)) ## one special ##' splitForm(~x+y+(diag(f|g))) ## 'hidden' special ##' splitForm(~x+y+(f|g)+cs(1|g)) ## combination ##' splitForm(~x+y+(1|f/g)) ## 'slash'; term ##' splitForm(~x+y+(1|f/g/h)) ## 'slash'; term ##' splitForm(~x+y+(1|(f/g)/h)) ## 'slash'; term ##' splitForm(~x+y+(f|g)+cs(1|g)+cs(a|b,stuff)) ## complex special ##' splitForm(~(((x+y)))) ## lots of parentheses ##' splitForm(~1+rr(f|g,n=2)) ##' ##' @author Steve Walker ##' @importFrom lme4 nobars ##' @export splitForm <- function(formula, defaultTerm="us", allowFixedOnly=TRUE, allowNoSpecials=TRUE, debug=FALSE) { ## logic: ## string for error message *if* specials not allowed ## (probably package-specific) noSpecialsAlt <- "lmer or glmer" specials <- findReTrmClasses() ## formula <- expandDoubleVerts(formula) ## split formula into separate ## random effects terms ## (including special terms) fbxx <- fbx(formula,debug,specials) formSplits <- expandAllGrpVar(fbxx) if (length(formSplits)>0) { formSplitID <- sapply(lapply(formSplits, "[[", 1), as.character) # warn about terms without a # setReTrm method ## FIXME:: do we need all of this?? if (FALSE) { badTrms <- formSplitID == "|" ## if(any(badTrms)) { ## stop("can't find setReTrm method(s)\n", ## "use findReTrmClasses() for available methods") ## FIXME: coerce bad terms to default as attempted below ## warning(paste("can't find setReTrm method(s) for term number(s)", ## paste(which(badTrms), collapse = ", "), ## "\ntreating those terms as unstructured")) formSplitID[badTrms] <- "(" fixBadTrm <- function(formSplit) { makeOp(formSplit[[1]],quote(`(`)) ## as.formula(paste(c("~(", as.character(formSplit)[c(2, 1, 3)], ")"), ## collapse = " "))[[2]] } formSplits[badTrms] <- lapply(formSplits[badTrms], fixBadTrm) } ## skipped parenTerm <- formSplitID == "(" # capture additional arguments reTrmAddArgs <- lapply(formSplits, "[", -2)[!parenTerm] # remove these additional # arguments formSplits <- lapply(formSplits, "[", 1:2) # standard RE terms formSplitStan <- formSplits[parenTerm] # structured RE terms formSplitSpec <- formSplits[!parenTerm] if (!allowNoSpecials) { if(length(formSplitSpec) == 0) stop( "no special covariance structures. ", "please use ",noSpecialsAlt, " or use findReTrmClasses() for available structures.") } reTrmFormulas <- c(lapply(formSplitStan, "[[", 2), lapply(formSplitSpec, "[[", 2)) reTrmClasses <- c(rep(defaultTerm, length(formSplitStan)), sapply(lapply(formSplitSpec, "[[", 1), as.character)) } else { reTrmFormulas <- reTrmAddArgs <- reTrmClasses <- NULL } fixedFormula <- noSpecials(nobars(formula)) list(fixedFormula = fixedFormula, reTrmFormulas = reTrmFormulas, reTrmAddArgs = reTrmAddArgs, reTrmClasses = reTrmClasses) } ##' @param term language object ##' @rdname splitForm ##' @param debug debugging mode (print stuff)? ##' @examples ##' noSpecials(y~1+us(1|f)) ##' noSpecials(y~1+us(1|f),delete=FALSE) ##' noSpecials(y~us(1|f)) ##' noSpecials(y~us+1) ## should *not* delete unless head of a function ##' @export ##' @keywords internal noSpecials <- function(term, delete=TRUE, debug=FALSE) { nospec <- noSpecials_(term, delete=delete, debug=debug) if (inherits(term, "formula") && length(term) == 3 && is.symbol(nospec)) { ## called with two-sided RE-only formula: ## construct response~1 formula as.formula(substitute(R~1,list(R=nospec)), env=environment(term)) } else nospec } ## noSpecials_(y~1+us(1|f)) noSpecials_ <- function(term,delete=TRUE, debug=FALSE) { if (debug) print(term) if (!anySpecial(term)) return(term) if (length(term)==1) return(term) ## 'naked' specials if (isSpecial(term)) { if(delete) { NULL } else { ## careful to return (1|f) and not 1|f: substitute((TERM), list(TERM = term[[2]])) } } else { nb2 <- noSpecials(term[[2]], delete=delete, debug=debug) nb3 <- if (length(term)==3) { noSpecials(term[[3]], delete=delete, debug=debug) } else NULL if (is.null(nb2)) nb3 else if (is.null(nb3)) nb2 else { term[[2]] <- nb2 term[[3]] <- nb3 term } } } isSpecial <- function(term) { if(is.call(term)) { ## %in% doesn't work (requires vector args) for(cls in findReTrmClasses()) { if(term[[1]] == cls) return(TRUE) } } FALSE } isAnyArgSpecial <- function(term) { for(tt in term) if(isSpecial(tt)) return(TRUE) FALSE } ## This could be in principle be fooled by a term with a matching name ## but this case is caught in noSpecials_() where we test for length>1 anySpecial <- function(term) { any(findReTrmClasses() %in% all.names(term)) } ##' test formula: does it contain a particular element? ##' @rdname formFuns ##' @examples ##' inForm(z~.,quote(.)) ##' inForm(z~y,quote(.)) ##' inForm(z~a+b+c,quote(c)) ##' inForm(z~a+b+(d+e),quote(c)) ##' f <- ~ a + offset(x) ##' f2 <- z ~ a ##' inForm(f,quote(offset)) ##' inForm(f2,quote(offset)) ##' @export ##' @keywords internal inForm <- function(form,value) { if (any(sapply(form,identical,value))) return(TRUE) if (all(sapply(form,length)==1)) return(FALSE) return(any(vapply(form,inForm,value,FUN.VALUE=logical(1)))) } ##' extract terms with a given head from an expression/formula ##' @rdname formFuns ##' @param term expression/formula ##' @param value head of terms to extract ##' @return a list of expressions ##' @examples ##' extractForm(~a+offset(b),quote(offset)) ##' extractForm(~c,quote(offset)) ##' extractForm(~a+offset(b)+offset(c),quote(offset)) ##' @export ##' @keywords internal extractForm <- function(term,value) { if (!inForm(term,value)) return(NULL) if (is.name(term) || !is.language(term)) return(NULL) if (identical(head(term),value)) { return(term) } if (length(term) == 2) { return(extractForm(term[[2]],value)) } return(c(extractForm(term[[2]],value), extractForm(term[[3]],value))) } ##' return a formula/expression with a given value stripped, where ##' it occurs as the head of a term ##' @rdname formFuns ##' @examples ##' dropHead(~a+offset(b),quote(offset)) ##' dropHead(~a+poly(x+z,3)+offset(b),quote(offset)) ##' @export ##' @keywords internal dropHead <- function(term,value) { if (!inForm(term,value)) return(term) if (is.name(term) || !is.language(term)) return(term) if (identical(head(term),value)) { return(term[[2]]) } if (length(term) == 2) { return(dropHead(term[[2]],value)) } else if (length(term) == 3) { term[[2]] <- dropHead(term[[2]],value) term[[3]] <- dropHead(term[[3]],value) return(term) } else stop("length(term)>3") } ## UNUSED (same function as drop.special2?) # drop.special(x~a + b+ offset(z)) drop.special <- function(term,value=quote(offset)) { if (length(term)==2 && identical(term[[1]],value)) return(NULL) if (length(term)==1) return(term) ## recurse, treating unary and binary operators separately nb2 <- drop.special(term[[2]]) nb3 <- if (length(term)==3) { drop.special(term[[3]]) } else NULL if (is.null(nb2)) ## RHS was special-only nb3 else if (is.null(nb3)) ## LHS was special-only nb2 else { ## insert values into daughters and return term[[2]] <- nb2 term[[3]] <- nb3 return(term) } } ##' drop terms matching a particular value from an expression ##' @rdname formFuns ## from Gabor Grothendieck: recursive solution ## http://stackoverflow.com/questions/40308944/removing-offset-terms-from-a-formula ##' @param x formula ##' @param value term to remove from formula ##' @param preserve (integer) retain the specified occurrence of "value" ##' @keywords internal drop.special2 <- function(x, value=quote(offset), preserve = NULL) { k <- 0 proc <- function(x) { if (length(x) == 1) return(x) if (x[[1]] == value && !((k <<- k+1) %in% preserve)) return(x[[1]]) replace(x, -1, lapply(x[-1], proc)) } ## handle 1- and 2-sided formulas if (length(x)==2) { newform <- substitute(~ . -x, list(x=value)) } else { newform <- substitute(. ~ . - x, list(x=value)) } return(update(proc(x), newform)) } ## Sparse Schur complement (Marginal of precision matrix) ##' @importFrom Matrix Cholesky solve GMRFmarginal <- function(Q, i, ...) { ind <- seq_len(nrow(Q)) i1 <- (ind)[i] i0 <- setdiff(ind, i1) if (length(i0) == 0) return(Q) Q0 <- as(Q[i0, i0, drop = FALSE], "symmetricMatrix") L0 <- Cholesky(Q0, ...) ans <- Q[i1, i1, drop = FALSE] - Q[i1, i0, drop = FALSE] %*% solve(Q0, as.matrix(Q[i0, i1, drop = FALSE])) ans } # n.b. won't work for terms with more than 2 args ... # @examples # replaceForm(quote(a(b+x*c(y,z))),quote(y),quote(R)) # ss <- ~(1 | cask:batch) + (1 | batch) # replaceForm(ss,quote(cask:batch),quote(batch:cask)) replaceForm <- function(term,target,repl) { if (identical(term,target)) return(repl) if (!inForm(term,target)) return(term) if (length(term) == 2) { return(substitute(OP(x),list(OP=term[[1]],x=replaceForm(term[[2]],target,repl)))) } return(substitute(OP(x,y),list(OP=term[[1]], x=replaceForm(term[[2]],target,repl), y=replaceForm(term[[3]],target,repl)))) } parallel_default <- function(parallel=c("no","multicore","snow"),ncpus=1) { ## boilerplate parallel-handling stuff, copied from lme4 if (missing(parallel)) parallel <- getOption("profile.parallel", "no") parallel <- match.arg(parallel) do_parallel <- (parallel != "no" && ncpus > 1L) if (do_parallel && parallel == "multicore" && .Platform$OS.type == "windows") { warning("no multicore on Windows, falling back to non-parallel") parallel <- "no" } return(list(parallel=parallel,do_parallel=do_parallel)) } ##' translate vector of correlation parameters to correlation values ##' @param theta vector of internal correlation parameters ##' @return a vector of correlation values ##' @details This function follows the definition at \url{http://kaskr.github.io/adcomp/classUNSTRUCTURED__CORR__t.html}: ##' if \eqn{L} is the lower-triangular matrix with 1 on the diagonal and the correlation parameters in the lower triangle, then the correlation matrix is defined as \eqn{\Sigma = D^{-1/2} L L^\top D^{-1/2}}{Sigma = sqrt(D) L L' sqrt(D)}, where \eqn{D = \textrm{diag}(L L^\top)}{D = diag(L L')}. For a single correlation parameter \eqn{\theta_0}{theta0}, this works out to \eqn{\rho = \theta_0/\sqrt{1+\theta_0^2}}{rho = theta0/sqrt(1+theta0^2)}. The function returns the elements of the lower triangle of the correlation matrix, in column-major order. ##' @examples ##' th0 <- 0.5 ##' stopifnot(all.equal(get_cor(th0),th0/sqrt(1+th0^2))) ##' get_cor(c(0.5,0.2,0.5)) ##' @export get_cor <- function(theta) { n <- round((1 + sqrt(1+8*length(theta)))/2) ## dim of cor matrix L <- diag(n) L[lower.tri(L)] <- theta cL <- tcrossprod(L) Dh <- diag(1/sqrt(diag(cL))) cc <- Dh %*% cL %*% Dh return(cc[lower.tri(cc)]) } match_which <- function(x,y) { which(sapply(y,function(z) x %in% z)) } ## reassign predvars to have term vars in the right order, ## but with 'predvars' values inserted where appropriate fix_predvars <- function(pv,tt) { if (length(tt)==3) { ## convert two-sided to one-sided formula tt <- RHSForm(tt, as.form=TRUE) } ## ugh, deparsing again ... tt_vars <- vapply(attr(tt,"variables"),deparse,character(1))[-1] ## remove terminal paren - e.g. match term poly(x, 2) to ## predvar poly(x, 2, ) ## beginning of string, including open-paren, colon ## and *first* comma but not arg ... init_regexp <- "^([(^:_.[:alnum:]]+).*" tt_vars_short <- gsub(init_regexp,"\\1",tt_vars) if (is.null(pv) || length(tt_vars)==0) return(NULL) new_pv <- quote(list()) ## maybe multiple variables per pv term ... [[-1]] ignores head ## FIXME: test for really long predvar strings ???? pv_strings <- vapply(pv,deparse,FUN.VALUE=character(1), width.cutoff=500)[-1] pv_strings <- gsub(init_regexp,"\\1",pv_strings) for (i in seq_along(tt_vars)) { w <- match(tt_vars_short[[i]],pv_strings) if (!is.na(w)) { new_pv[[i+1]] <- pv[[w+1]] } else { ## insert symbol from term vars new_pv[[i+1]] <- as.symbol(tt_vars[[i]]) } } return(new_pv) } hasRandom <- function(x) { pl <- getParList(x) return(length(unlist(pl[grep("^theta",names(pl))]))>0) } getParms <- function(parm=NULL, object, full=FALSE) { vv <- vcov(object, full=TRUE) sds <- sqrt(diag(vv)) pnames <- names(sds) <- rownames(vv) intnames <- names(object$obj$env$last.par) ## internal names ## "beta" vals may be identified by object$obj$env$random, if REML intnames <- intnames[intnames != "b"] if (length(pnames) != length(sds)) { ## shouldn't happen ... stop("length mismatch between internal and external parameter names") } if (is.null(parm)) { if (!full && trivialDisp(object)) { parm <- grep("betad", intnames, invert=TRUE) } else { parm <- seq_along(sds) } } if (is.character(parm)) { if (identical(parm,"theta_")) { parm <- which(intnames=="theta") } else if (identical(parm,"beta_")) { if (trivialDisp(object)) { ## include conditional and zi params ## but not dispersion params parm <- grep("^beta(zi)?$",intnames) } else { parm <- grep("beta",intnames) } } else if (identical(parm, "disp_") || identical(parm, "sigma")) { parm <- grep("^betad", intnames) } else { ## generic parameter vector nparm <- match(parm,pnames) if (any(is.na(nparm))) { stop("unrecognized parameter names: ", parm[is.na(nparm)]) } parm <- nparm } } return(parm) } isREML <- function(x) { if (is.null(REML <- x$modelInfo$REML)) { ## let vcov work with old (pre-REML option) stored objects REML <- FALSE } return(REML) } glmmTMB/R/zzz.R0000644000176200001440000000056613614324717012751 0ustar liggesusers## Startup code # register emmeans methods dynamically .onLoad <- function(libname, pkgname) { if (requireNamespace("emmeans", quietly = TRUE)) { if (utils::packageVersion("emmeans") < "1.4") { warning("please install a newer version of emmeans (> 1.4)") return(NULL) } emmeans::.emm_register("glmmTMB", pkgname) } } glmmTMB/R/VarCorr.R0000644000176200001440000003511713614324717013472 0ustar liggesusers## returns a true family() object iff one was given ## to glmmTMB() in the first place .... ##' @importFrom stats family ##' @export family.glmmTMB <- function(object, ...) { object$modelInfo$family } ## don't quite know why this (rather than just ...$parList()) is ## necessary -- used in ranef.glmmTMB and sigma.glmmTMB getParList <- function(object) { object$obj$env$parList(object$fit$par, object$fit$parfull) } ##' Extract residual standard deviation or dispersion parameter ##' ##' For Gaussian models, \code{sigma} returns the value of the residual ##' standard deviation; for other families, it returns the ##' dispersion parameter, \emph{however it is defined for that ##' particular family}. See details for each family below. ##' ##' @details ##' The value returned varies by family: ##' \describe{ ##' \item{gaussian}{returns the \emph{maximum likelihood} estimate ##' of the standard deviation (i.e., smaller than the results of ##' \code{sigma(lm(...))} by a factor of (n-1)/n)} ##' \item{nbinom1}{returns an overdispersion parameter ##' (usually denoted \eqn{\alpha}{alpha} as in Hardin and Hilbe (2007)): ##' such that the variance equals \eqn{\mu(1+\alpha)}{mu(1+alpha)}.} ##' \item{nbinom2}{returns an overdispersion parameter ##' (usually denoted \eqn{\theta}{theta} or \eqn{k}); in contrast to ##' most other families, larger \eqn{\theta}{theta} corresponds to a \emph{lower} ##' variance which is \eqn{\mu(1+\mu/\theta)}{mu(1+mu/theta)}.} ##' \item{Gamma}{Internally, glmmTMB fits Gamma responses by fitting a mean ##' and a shape parameter; sigma is estimated as (1/sqrt(shape)), ##' which will typically be close (but not identical to) that estimated ##' by \code{stats:::sigma.default}, which uses sqrt(deviance/df.residual)} ##' \item{beta}{returns the value of \eqn{\phi}{phi}, ##' where the conditional variance is \eqn{\mu(1-\mu)/(1+\phi)}{mu*(1-mu)/(1+phi)} ##' (i.e., increasing \eqn{\phi}{phi} decreases the variance.) ##' This parameterization follows Ferrari and Cribari-Neto (2004) ##' (and the \code{betareg} package):} ##' \item{betabinomial}{This family uses the same parameterization (governing ##' the Beta distribution that underlies the binomial probabilities) as \code{beta}.} ##' \item{genpois}{returns the index of dispersion \eqn{\phi^2}{phi^2}, ##' where the variance is \eqn{\mu\phi^2}{mu*phi^2} (Consul & Famoye 1992)} ##' \item{compois}{returns the value of \eqn{1/\nu}{1/nu}, ##' When \eqn{\nu=1}{nu=1}, compois is equivalent to the Poisson distribution. ##' There is no closed form equation for the variance, but ##' it is approximately undersidpersed when \eqn{1/\nu <1}{1/nu <1} ##' and approximately oversidpersed when \eqn{1/\nu >1}{1/nu>1}. ##' In this implementation, \eqn{\mu}{mu} is exactly the mean (Huang 2017), which ##' differs from the COMPoissonReg package (Sellers & Lotze 2015).} ##' \item{tweedie}{returns the value of \eqn{\phi}{phi}, ##' where the variance is \eqn{\phi\mu^p}{phi*mu^p}. ##' The value of \eqn{p} can be extracted using the internal ##' function \code{glmmTMB:::.tweedie_power}.} ##' } ##' ##' The most commonly used GLM families ##' (\code{binomial}, \code{poisson}) have fixed dispersion parameters which are ##' internally ignored. ##' ##' @references ##' \itemize{ ##' \item Consul PC, and Famoye F (1992). "Generalized Poisson regression model. Communications in Statistics: Theory and Methods" 21:89–109. ##' \item Ferrari SLP, Cribari-Neto F (2004). "Beta Regression for Modelling Rates and Proportions." \emph{J. Appl. Stat.} 31(7), 799-815. ##' \item Hardin JW & Hilbe JM (2007). "Generalized linear models and extensions." Stata press. ##' \item Huang A (2017). "Mean-parametrized Conway–Maxwell–Poisson regression models for dispersed counts. " \emph{Statistical Modelling} 17(6), 1-22. ##' \item Sellers K & Lotze T (2015). "COMPoissonReg: Conway-Maxwell Poisson (COM-Poisson) Regression". R package version 0.3.5. https://CRAN.R-project.org/package=COMPoissonReg ##' } ##' @aliases sigma ##' @param object a \dQuote{glmmTMB} fitted object ##' @param \dots (ignored; for method compatibility) ## Import generic and re-export ## note the following line is hacked in Makefile/namespace-update to ... ## if(getRversion()>='3.3.0') importFrom(stats, sigma) else importFrom(lme4,sigm ## also see ##' @rawNamespace if(getRversion()>='3.3.0') importFrom(stats, sigma) else importFrom(lme4,sigma) ## n.b. REQUIRES roxygen2 >= 5.0 ## @importFrom lme4 sigma ##' @export sigma ##' @method sigma glmmTMB ##' @export sigma.glmmTMB <- function(object, ...) { pl <- getParList(object) ff <- object$modelInfo$family$family if (!usesDispersion(ff)) return(1.) if (length(pl$betad)>1) return(NA) switch(family(object)$family, gaussian=exp(0.5*pl$betad), Gamma=exp(-0.5*pl$betad), exp(pl$betad)) } mkVC <- function(cor, sd, cnms, sc, useSc) { stopifnot(length(cnms) == (nc <- length(cor)), nc == length(sd), is.list(cnms), is.list(cor), is.list(sd), is.character(nnms <- names(cnms)), nzchar(nnms)) ## ## FIXME: do we want this? Maybe not. ## Potential problem: the names of the elements of the VarCorr() list ## are not necessarily unique (e.g. fm2 from example("lmer") has *two* ## Subject terms, so the names are "Subject", "Subject". The print method ## for VarCorrs handles this just fine, but it's a little awkward if we ## want to dig out elements of the VarCorr list ... ??? if (anyDuplicated(nnms)) nnms <- make.names(nnms, unique = TRUE) ## ## cov := F(sd, cor) : do1cov <- function(sd, cor, n = length(sd)) { sd * cor * rep(sd, each = n) } docov <- function(sd,cor,nm) { ## diagonal model: diagmodel <- identical(dim(cor),c(0L,0L)) if (diagmodel) cor <- diag(length(sd)) cov <- do1cov(sd, cor) names(sd) <- nm dimnames(cov) <- dimnames(cor) <- list(nm,nm) structure(cov,stddev=sd,correlation=cor) } ss <- setNames(mapply(docov,sd,cor,cnms,SIMPLIFY=FALSE),nnms) ## ONLY first element -- otherwise breaks formatVC ## FIXME: do we want a message/warning here, or elsewhere, ## when the 'Residual' var parameters are truncated? attr(ss,"sc") <- sc[1] attr(ss,"useSc") <- useSc ss } ##' Extract variance and correlation components ##' ##' @aliases VarCorr ##' @param x a fitted \code{glmmTMB} model ##' @param sigma residual standard deviation (usually set automatically from internal information) ##' @param extra arguments (for consistency with generic method) ##' @importFrom nlme VarCorr ## and re-export the generic: ##' @export VarCorr ##' @export ##' @examples ##' ## Comparing variance-covariance matrix with manual computation ##' data("sleepstudy",package="lme4") ##' fm4 <- glmmTMB(Reaction ~ Days + (Days|Subject), sleepstudy) ##' VarCorr(fm4)[[c("cond","Subject")]] ##' ## hand calculation ##' pars <- getME(fm4,"theta") ##' ## construct cholesky factor ##' L <- diag(2) ##' L[lower.tri(L)] <- pars[-(1:2)] ##' C <- crossprod(L) ##' diag(C) <- 1 ##' sdvec <- exp(pars[1:2]) ##' (V <- outer(sdvec,sdvec) * C) ##' @details For an unstructured variance-covariance matrix, the internal parameters ##' are structured as follows: the first n parameters are the log-standard-deviations, ##' while the remaining n(n-1)/2 parameters are the elements of the Cholesky factor ##' of the correlation matrix, filled in column-wise order ##' (see the \href{http://kaskr.github.io/adcomp/classUNSTRUCTURED__CORR__t.html}{TMB documentation} ##' for further details). ##' @keywords internal VarCorr.glmmTMB <- function(x, sigma = 1, ... ) { ## FIXME:: add type=c("varcov","sdcorr","logs" ?) ## FIXME:: do we need 'sigma' any more (now that nlme generic ## doesn't have it?) stopifnot(is.numeric(sigma), length(sigma) == 1) xrep <- x$obj$env$report(x$fit$parfull) reT <- x$modelInfo$reTrms reS <- x$modelInfo$reStruc familyStr <- family(x)$family useSc <- if (missing(sigma)) { ## *only* report residual variance for Gaussian family ... ## *not* usesDispersion(familyStr) sigma <- sigma(x) familyStr=="gaussian" && !zeroDisp(x) } else TRUE vc.cond <- vc.zi <- NULL if(length(cn <- reT$cond$cnms)) { vc.cond <- mkVC(cor = xrep$corr, sd = xrep$sd, cnms = cn, sc = sigma, useSc = useSc) for (i in seq_along(vc.cond)) { attr(vc.cond[[i]],"blockCode") <- reS$condReStruc[[i]]$blockCode } } if(length(cn <- reT$zi$cnms)) { vc.zi <- mkVC(cor = xrep$corrzi, sd = xrep$sdzi, cnms = cn, sc = sigma, useSc = useSc) for (i in seq_along(vc.zi)) { attr(vc.zi,"blockCode") <- reS$ziReStruc[[i]]$blockCode } } structure(list(cond = vc.cond, zi = vc.zi), sc = usesDispersion(familyStr), ## 'useScale' class = "VarCorr.glmmTMB") } ##' Printing The Variance and Correlation Parameters of a \code{glmmTMB} ##' @method print VarCorr.glmmTMB ##' @export ## don't importFrom lme4 formatVC; use our own formatV instead! ## document as it is a method with "surprising arguments": ##' @param x a result of \code{\link{VarCorr}()}. ##' @param digits number of significant digits to use. ##' @param comp a string specifying the component to format and print. ##' @param formatter a \code{\link{function}}. ##' @param ... optional further arguments, passed the next \code{\link{print}} method. print.VarCorr.glmmTMB <- function(x, digits = max(3, getOption("digits") - 2), comp = "Std.Dev.", formatter = format, ...) { for (cc in names(x)) if(!is.null(x[[cc]])) { cat(sprintf("\n%s:\n", cNames[[cc]])) print(formatVC(x[[cc]], digits = digits, comp = comp, formatter = formatter), quote = FALSE, ...) } invisible(x) } ## original from lme4 ## had to be extended/modified to deal with glmmTMB special cases ##' "format()" the 'VarCorr' matrix of the random effects -- for ##' print()ing and show()ing ##' ##' @title Format the 'VarCorr' Matrix of Random Effects ##' @param varcor a \code{\link{VarCorr}} (-like) matrix with attributes. ##' @param digits the number of significant digits. ##' @param comp character vector of length one or two indicating which ##' columns out of "Variance" and "Std.Dev." should be shown in the ##' formatted output. ##' @param formatter the \code{\link{function}} to be used for ##' formatting the standard deviations and or variances (but ##' \emph{not} the correlations which (currently) are always formatted ##' as "0.nnn" ##' @param useScale whether to report a scale parameter (e.g. residual standard deviation) ##' @param ... optional arguments for \code{formatter(*)} in addition ##' to the first (numeric vector) and \code{digits}. ##' @return a character matrix of formatted VarCorr entries from \code{varc}. ##' @importFrom methods as formatVC <- function(varcor, digits = max(3, getOption("digits") - 2), comp = "Std.Dev.", formatter = format, useScale = attr(varcor, "useSc"), ...) { c.nms <- c("Groups", "Name", "Variance", "Std.Dev.") avail.c <- c.nms[-(1:2)] if(anyNA(mcc <- pmatch(comp, avail.c))) stop("Illegal 'comp': ", comp[is.na(mcc)]) nc <- length(colnms <- c(c.nms[1:2], (use.c <- avail.c[mcc]))) if(length(use.c) == 0) stop("Must show either standard deviations or variances") formatCor <- function(x,maxlen=0) { ## x: correlation matrix ## maxlen: max number of RE std devs per term; ## really a *minimum* length here! pad to (maxlen) ## columns as necessary x <- as(x, "matrix") dig <- max(2, digits - 2) # use 'digits' ! ## n.b. not using formatter() for correlations cc <- format(round(x, dig), nsmall = dig) cc[!lower.tri(cc)] <- "" ## empty lower triangle nr <- nrow(cc) if (nr < maxlen) { cc <- cbind(cc, matrix("", nr, maxlen-nr)) } return(cc) } getCovstruct <- function(x) { n <- names(.valid_covstruct)[match(attr(x,"blockCode"), .valid_covstruct)] if (length(n)==0) n <- "us" ## unstructured v-cov (default) return(n) } getCorSD <- function(x,type="stddev",maxlen=0) { r <- attr(x,type) ## extract stddev *or* correlation from x if (type=="correlation") { ## transform to char matrix r <- formatCor(r,maxlen) ## drop last column (will be blank since we blanked out ## the upper triangle + diagonal) if (ncol(r)>maxlen) r <- r[, -ncol(r), drop = FALSE] } covstruct <- getCovstruct(x) if (covstruct %in% c("ar1","cs")) { r <- switch(type, stddev=r[1], ## select lag-1 correlation ## upper tri has been erased in formatCor() ... correlation=paste(r[2,1],sprintf("(%s)",covstruct)) ) } return(r) } ## getCorSD ## get std devs: reStdDev <- lapply(varcor, getCorSD) ## need correlations if useCor <- (sapply(varcor,getCovstruct)!="us" | sapply(reStdDev,length)>1) cnms <- Map(function(x,n) colnames(x)[seq(n)], varcor, lengths(reStdDev)) if(useScale) { reStdDev <- c(reStdDev, list(Residual = unname(attr(varcor, "sc")))) } reLens <- lengths(reStdDev) nr <- sum(reLens) reMat <- array('', c(nr, nc), list(rep.int('', nr), colnms)) reMat[1+cumsum(reLens)-reLens, "Groups"] <- names(reLens) reMat[,"Name"] <- c(unlist(cnms), if(useScale) "") if("Variance" %in% use.c) reMat[,"Variance"] <- formatter(unlist(reStdDev)^2, digits = digits, ...) if("Std.Dev." %in% use.c) reMat[,"Std.Dev."] <- formatter(unlist(reStdDev), digits = digits, ...) if (any(useCor)) { ## get corrs maxlen <- max(reLens) corr <- do.call(rbind,lapply(varcor, getCorSD, type="correlation", maxlen=maxlen)) ## add blank values as necessary if (nrow(corr) < nrow(reMat)) corr <- rbind(corr, matrix("", nrow(reMat) - nrow(corr), ncol(corr))) colnames(corr) <- c("Corr", rep.int("", max(0L, ncol(corr)-1L))) cbind(reMat, corr) } else reMat } glmmTMB/R/effects.R0000644000176200001440000000276713614324717013540 0ustar liggesusers## modified from contribution by Sanford Weisberg ##' @rdname downstream_methods ##' @param focal.predictors a character vector of one or more predictors in the ##' model in any order. ##' ##' @rawNamespace if(getRversion() >= "3.6.0") { ##' S3method(effects::Effect, glmmTMB) ##' } else { ##' export(Effect.glmmTMB) ##' } Effect.glmmTMB <- function (focal.predictors, mod, ...) { fam <- family(mod) ## code to make the 'truncated_*' families work if (grepl("^truncated", fam$family)) fam <- c(fam, make.link(fam$link)) ## dummy functions to make Effect.default work dummyfuns <- list(variance=function(mu) mu, initialize=expression(mustart <- y + 0.1), dev.resids=function(...) poisson()$dev.res(...) ) for (i in names(dummyfuns)) { if (is.null(fam[[i]])) fam[[i]] <- dummyfuns[[i]] } ## allow calculation of effects ... if (length(formals(fam$variance))>1) { warning("overriding variance function for effects: ", "computed variances may be incorrect") fam$variance <- dummyfuns$variance } args <- list(call = getCall(mod), coefficients = lme4::fixef(mod)[["cond"]], vcov = vcov(mod)[["cond"]], family=fam) if (!requireNamespace("effects")) stop("please install the effects package") effects::Effect.default(focal.predictors, mod, ..., sources = args) } glmmTMB/R/predict.R0000644000176200001440000003525213614324717013546 0ustar liggesusers## Helper function for predict. ## Assert that we can use old model (data.tmb0) as basis for ## predictions using the new data (data.tmb1): assertIdenticalModels <- function(data.tmb1, data.tmb0, allow.new.levels=FALSE) { ## Check terms. Only 'blockReps' and 'blockSize' are allowed to ## change. Note that we allow e.g. spatial covariance matrices to ## change, while e.g. an unstrucured covariance must remain the ## same. checkTerms <- function(t1, t0) { ## Defensive check: stopifnot(identical(names(t1), names(t0))) ## *Never* allowed to differ: testIdentical <- function(checkNm) { unlist( Map( function(x,y) identical(x[checkNm], y[checkNm]), t0, t1) ) } ok <- testIdentical( c("blockNumTheta", "blockCode") ) if ( ! all(ok) ) { msg <- c("Prediction is not possible for terms: ", paste(names(t1)[!ok], collapse=", "), "\n", "Probably some factor levels in 'newdata' require fitting a new model.") stop(msg) } ## Sometimes allowed to differ: if ( ! allow.new.levels ) { ok <- testIdentical( c( "blockReps", "blockSize") ) if ( ! all(ok) ) { msg <- c("Predicting new random effect levels for terms: ", paste(names(t1)[!ok], collapse=", "), "\n", "Disable this warning with 'allow.new.levels=TRUE'") ## FIXME: warning or error ? warning(msg) } } } checkTerms( data.tmb1$terms, data.tmb0$terms ) checkTerms( data.tmb1$termszi, data.tmb0$termszi ) ## Fixed effect parameters must be identical checkModelMatrix <- function(X1, X0) { if( !identical(colnames(X1), colnames(X0)) ) { msg <- c("Prediction is not possible for unknown fixed effects: ", paste( setdiff(colnames(X1), colnames(X0)), collapse=", "), "\n", "Probably some factor levels in 'newdata' require fitting a new model.") stop(msg) } } checkModelMatrix(data.tmb1$X, data.tmb0$X) checkModelMatrix(data.tmb1$Xzi, data.tmb0$Xzi) NULL } ##' prediction ##' @param object a \code{glmmTMB} object ##' @param newdata new data for prediction ##' @param newparams new parameters for prediction ##' @param se.fit return the standard errors of the predicted values? ##' @param zitype deprecated: formerly used to specify type of zero-inflation probability. Now synonymous with \code{type} ##' @param type Denoting \eqn{mu} as the mean of the conditional distribution and ##' \code{p} as the zero-inflation probability, ##' the possible choices are: ##' \describe{ ##' \item{"link"}{conditional mean on the scale of the link function, ##' or equivalently the linear predictor of the conditional model} ##' \item{"response"}{expected value; this is \eqn{mu*(1-p)} for zero-inflated models ##' and \code{mu} otherwise} ##' \item{"conditional"}{mean of the conditional response; \code{mu} for all models ##' (i.e., synonymous with \code{"response"} in the absence of zero-inflation} ##' \item{"zprob"}{the probability of a structural zero (gives an error ##' for non-zero-inflated models)} ##' \item{"zlink"}{predicted zero-inflation probability on the scale of ##' the logit link function} ##' \item{"disp"}{dispersion parameter however it is defined for that particular family as described in \code{\link{sigma.glmmTMB}}} ##' } ##' @param na.action how to handle missing values in \code{newdata} (see \code{\link{na.action}}); ##' the default (\code{na.pass}) is to predict \code{NA} ##' @param debug (logical) return the \code{TMBStruc} object that will be ##' used internally for debugging? ##' @param re.form \code{NULL} to specify individual-level predictions; \code{~0} or \code{NA} to specify population-level predictions (i.e., setting all random effects to zero) ##' @param allow.new.levels allow previously unobserved levels in random-effects variables? see details. ##' @param \dots unused - for method compatibility ##' @details ##' \itemize{ ##' \item To compute population-level predictions for a given grouping variable (i.e., setting all random effects for that grouping variable to zero), set the grouping variable values to \code{NA}. Finer-scale control of conditioning (e.g. allowing variation among groups in intercepts but not slopes when predicting from a random-slopes model) is not currently possible. ##' \item Prediction of new random effect levels is possible as long as the model specification (fixed effects and parameters) is kept constant. ##' However, to ensure intentional usage, a warning is triggered if \code{allow.new.levels=FALSE} (the default). ##' \item Prediction using "data-dependent bases" (variables whose scaling or transformation depends on the original data, e.g. \code{\link{poly}}, \code{\link[splines]{ns}}, or \code{\link{poly}}) should work properly; however, users are advised to check results extra-carefully when using such variables. Models with different versions of the same data-dependent basis type in different components (e.g. \code{formula= y ~ poly(x,3), dispformula= ~poly(x,2)}) will probably \emph{not} produce correct predictions. ##' } ##' ##' @examples ##' data(sleepstudy,package="lme4") ##' g0 <- glmmTMB(Reaction~Days+(Days|Subject),sleepstudy) ##' predict(g0, sleepstudy) ##' ## Predict new Subject ##' nd <- sleepstudy[1,] ##' nd$Subject <- "new" ##' predict(g0, newdata=nd, allow.new.levels=TRUE) ##' ## population-level prediction ##' nd_pop <- data.frame(Days=unique(sleepstudy$Days), ##' Subject=NA) ##' predict(g0, newdata=nd_pop) ##' @importFrom TMB sdreport ##' @importFrom stats optimHess model.frame na.fail na.pass napredict contrasts<- ##' @export predict.glmmTMB <- function(object,newdata=NULL, newparams=NULL, se.fit=FALSE, re.form=NULL, allow.new.levels=FALSE, type = c("link", "response", "conditional","zprob","zlink", "disp"), zitype = NULL, na.action = na.pass, debug=FALSE, ...) { ## FIXME: add re.form if (!is.null(zitype)) { warning("zitype is deprecated: please use type instead") type <- zitype } type <- match.arg(type) ## FIXME: better test? () around re.form==~0 are *necessary* ## could steal isRE from lme4 predict.R ... pop_pred <- (!is.null(re.form) && ((re.form==~0) || identical(re.form,NA))) if (!(is.null(re.form) || pop_pred)) { stop("re.form must equal NULL, NA, or ~0") } mc <- mf <- object$call ## FIXME: DRY so much ## now work on evaluating model frame ## do we want to re-do this part??? ## need to 'fix' call to proper model.frame call whether or not ## we have new data, because ... (??) m <- match(c("subset", "weights", "offset", "na.action"), names(mf), 0L) mf <- mf[c(1L, m)] mf$drop.unused.levels <- TRUE mf[[1]] <- as.name("model.frame") ## substitute *combined* data frame, in hopes of getting all of the ## bits we need for any of the model frames ... tt <- terms(object$modelInfo$allForm$combForm) pv <- attr(terms(model.frame(object)),"predvars") attr(tt,"predvars") <- fix_predvars(pv,tt) mf$formula <- RHSForm(tt, as.form=TRUE) ## FIXME:: fix_predvars is ugly, and should be refactored. ## the best solution is probably to attach predvars information ## to formulas/terms for individual components ## {conditional, zi, disp} * {fixed, random} ## and fix things downstream, where the actual model matrices ## are constructed. ## ## There's a fairly high chance of breakage with crazy/unforeseen ## usage of data-dependent bases (e.g. polynomials or splines with ## different arguments in different parts of the model ...) ## Can we detect/warn about these? ## if (is.null(newdata)) { mf$data <- mc$data ## restore original data newFr <- object$frame } else { mf$data <- newdata mf$na.action <- na.action newFr <- eval.parent(mf) } omi <- object$modelInfo ## shorthand ("**o**bject$**m**odel**I**nfo") respCol <- match(respNm <- names(omi$respCol),names(newFr)) ## create *or* overwrite response column for prediction data with NA newFr[[respNm]] <- NA ## FIXME: not yet handling population-level predictions (re.form ## or new levels/allow.new.levels) ## append to existing model frame ## rbind loses attributes! ## https://stackoverflow.com/questions/46258816/copy-attributes-when-using-rbind ## at this point I'm not even sure if contrasts are actually *used* ## for anything in the prediction process: do mismatches even matter? safe_contrasts <- function(x) { if (length(levels(x))<2) return(NULL) else return(contrasts(x)) } aug_contrasts <- function(c1,new_levels) { rbind(c1, matrix(0, ncol=ncol(c1), nrow=length(new_levels), dimnames=list(new_levels,colnames(c1)))) } augFr <- rbind(object$frame,newFr) facs <- which(vapply(augFr,is.factor,logical(1))) for (fnm in names(augFr)[facs]) { c1 <- safe_contrasts(object$frame[[fnm]]) c2 <- safe_contrasts(newFr[[fnm]]) if (!allow.new.levels) { c1_sub <- c1[rownames(c2),colnames(c2),drop=FALSE] if (!is.null(c2) && ## maybe too coarse, but as mentioned above, I don't ## even know if such mismatches really matter ... !(isTRUE(all.equal(c1_sub,c2)) || isTRUE(all.equal(c1,c2)))) { stop("contrasts mismatch between original and prediction frame in variable ", sQuote(fnm)) } } ## DON'T check for contrasts mismatch with new levels ## (hope we don't miss anything important!) ## what do we do here? ## the new levels aren't actually going to get used for anything, ## but they break the contrast construction. Extend the contrast ## matrix with a properly labeled zero matrix. if (!is.null(c1)) { new_levels <- stats::na.omit(setdiff(unique(newFr[[fnm]]),levels(object$frame[[fnm]]))) contrasts(augFr[[fnm]]) <- aug_contrasts(c1,new_levels) } } ## Pointers into 'new rows' of augmented data frame. w <- nrow(object$fr) + seq_len(nrow(newFr)) ## Variety of possible binomial inputs are taken care of by ## 'mkTMBStruc' further down. yobs <- augFr[[names(omi$respCol)]] ## match type arg with internal name ## FIXME: warn if "link" ziPredNm <- switch(type, response = "corrected", link =, conditional= "uncorrected", zlink = , zprob = "prob", disp = "disp",#zi irrelevant; just reusing variable stop("unknown type ",type)) ziPredCode <- .valid_zipredictcode[ziPredNm] ## need eval.parent() because we will do eval(mf) down below ... TMBStruc <- ## FIXME: make first arg of mkTMBStruc into a formula list ## with() interfering with eval.parent() ? eval.parent(mkTMBStruc(RHSForm(omi$allForm$formula,as.form=TRUE), omi$allForm$ziformula, omi$allForm$dispformula, omi$allForm$combForm, mf, fr=augFr, yobs=yobs, respCol=respCol, weights=model.weights(augFr), contrasts=omi$contrasts, family=omi$family, ziPredictCode=ziPredNm, doPredict=as.integer(se.fit), whichPredict=w, REML=omi$REML, map=omi$map)) ## short-circuit if(debug) return(TMBStruc) ## Check that the model specification is unchanged: assertIdenticalModels(TMBStruc$data.tmb, object$obj$env$data, allow.new.levels) ## Check that the necessary predictor variables are finite (not NA nor NaN) if(se.fit) { with(TMBStruc$data.tmb, if(any(!is.finite(X)) | any(!is.finite(Z@x)) | any(!is.finite(Xzi)) | any(!is.finite(Zzi@x)) | any(!is.finite(Xd)) ) stop("Some variables in newdata needed for predictions contain NAs or NaNs. This is currently incompatible with se.fit=TRUE.")) } ## FIXME: what if newparams only has a subset of components? oldPar <- object$fit$par if (!is.null(newparams)) oldPar <- newparams if (pop_pred) { TMBStruc <- within(TMBStruc, { parameters$b[] <- 0 mapArg$b <- factor(rep(NA,length(parameters$b))) }) } newObj <- with(TMBStruc, MakeADFun(data.tmb, parameters, map = mapArg, random = randomArg, profile = NULL, # TODO: Optionally "beta" silent = TRUE, DLL = "glmmTMB")) newObj$fn(oldPar) ## call once to update internal structures lp <- newObj$env$last.par na.act <- attr(model.frame(object),"na.action") do.napred <- missing(newdata) && !is.null(na.act) if (!se.fit) { pred <- newObj$report(lp)$mu_predict } else { H <- with(object,optimHess(oldPar,obj$fn,obj$gr)) ## FIXME: Eventually add 'getReportCovariance=FALSE' to this sdreport ## call to fix memory issue (requires recent TMB version) ## Fixed! (but do we want a flag to get it ? ...) sdr <- sdreport(newObj,oldPar,hessian.fixed=H,getReportCovariance=FALSE) sdrsum <- summary(sdr, "report") ## TMB:::summary.sdreport(sdr, "report") pred <- sdrsum[,"Estimate"] se <- sdrsum[,"Std. Error"] } if (do.napred) { pred <- napredict(na.act,pred) if (se.fit) se <- napredict(na.act,se) } if (type %in% c("zlink","link")) { ff <- object$modelInfo$family if (!(type=="link" && ff$link=="identity")) { if (type=="zlink") { ff <- make.link("logit") } pred <- ff$linkfun(pred) if (se.fit) se <- se/ff$mu.eta(pred) ## do this after transforming pred! } ## if not identity link } ## if link or zlink if (!se.fit) return(pred) else return(list(fit=pred,se.fit=se)) } glmmTMB/R/utils_covstruct.R0000644000176200001440000000525613614324717015371 0ustar liggesusers## Workaround to associate numeric values with factor levels in a way ## that survives through the lme4 machinery. ##' Create a factor with numeric interpretable factor levels. ##' ##' Some \code{glmmTMB} covariance structures require extra ##' information, such as temporal or spatial ##' coordinates. \code{numFactor} allows to associate such extra ##' information as part of a factor via the factor levels. The ##' original numeric coordinates are recoverable without loss of ##' precision using the function \code{parseNumLevels}. Factor levels ##' are sorted coordinate wise from left to right: first coordinate is ##' fastest running. ##' @title Factor with numeric interpretable levels. ##' @param x Vector, matrix or data.frame that constitute the ##' coordinates. ##' @param ... Additional vectors, matrices or data.frames that ##' constitute the coordinates. ##' @return Factor with specialized coding of levels. ##' @examples ##' ## 1D example ##' numFactor(sample(1:5,20,TRUE)) ##' ## 2D example ##' coords <- cbind( sample(1:5,20,TRUE), sample(1:5,20,TRUE) ) ##' (f <- numFactor(coords)) ##' parseNumLevels(levels(f)) ## Sorted ##' ## Used as part of a model.matrix ##' model.matrix( ~f ) ##' ## parseNumLevels( colnames(model.matrix( ~f )) ) ##' ## Error: 'Failed to parse numeric levels: (Intercept)' ##' parseNumLevels( colnames(model.matrix( ~ f-1 )) ) ##' @export numFactor <- function(x, ...) { y <- data.frame(x, ...) if( !all( sapply(y, is.numeric) | sapply(y, is.factor)) ) stop("All arguments to 'numFactor' must be numeric or factor.") asChar <- function(y) { y <- lapply(y, as.character) ans <- do.call("paste", c(y, list(sep=","))) paste0("(", ans, ")") } fac <- asChar(y) ndup <- !duplicated(fac) y0 <- y[ndup, , drop=FALSE] for (col in seq_along(y0) ) { y0 <- y0[ order( y0[[col]] ), , drop=FALSE] } facLevels <- asChar(y0) factor( fac, levels = facLevels ) } ##' @rdname numFactor ##' @param levels Character vector to parse into numeric values. ##' @importFrom stats complete.cases ##' @export parseNumLevels <- function(levels) { ## Strip initial (irrelevant) characters: tmp <- sub("^.*(\\(.+\\))$", "\\1", levels) ## Now tmp must have the form ([0-9]*,[0-9]*,...) ## Otherwise it's an error tmp <- sub("^\\(", "", tmp) tmp <- sub("\\)$", "", tmp) ## Split string and convert to numeric ans <- lapply( strsplit(tmp, ","), as.numeric ) ans <- t( do.call("cbind", ans) ) ## if(any(is.na(ans))) stop("Failed to parse numeric levels.") if(any(is.na(ans))) { stop("Failed to parse numeric levels: ", levels[!complete.cases(ans)]) } ans } glmmTMB/R/Anova.R0000644000176200001440000002227013614324717013154 0ustar liggesusers## Type II and III tests for linear, generalized linear, and other models (J. Fox) ## most of what's below is copied from car::Anova.R ## main changes are (1) absence of F-test (K-R, Satterthwaite df) capability; ## (2) use of [[component]] to pick out relevant fixed effect parameters/v-cov matrix ## copied unchanged (?); unexported utilities from car responseName.default <- function (model, ...) deparse(attr(terms(model), "variables")[[2]]) term.names.default <- function (model, component="cond", ...) { term.names <- labels(terms(model, component=component)) if (has.intercept(model)) c("(Intercept)", term.names) else term.names } has.intercept <- function (model, ...) { UseMethod("has.intercept") } ConjComp <- function(X, Z = diag( nrow(X)), ip = diag(nrow(X))) { ## This function by Georges Monette ## finds the conjugate complement of the proj of X in span(Z) wrt ## inner product ip ## - assumes Z is of full column rank ## - projects X conjugately wrt ip into span Z xq <- qr(t(Z) %*% ip %*% X) if (xq$rank == 0) return(Z) Z %*% qr.Q(xq, complete = TRUE) [ ,-(1:xq$rank)] } relatives <- function(term, names, factors){ is.relative <- function(term1, term2) { all(!(factors[,term1]&(!factors[,term2]))) } if(length(names) == 1) return(NULL) which.term <- which(term==names) (1:length(names))[-which.term][sapply(names[-which.term], function(term2) is.relative(term, term2))] } ## modified has.intercept.glmmTMB <- function (model, component="cond", ...) { nms <- names(fixef(model)[[component]]) any(grepl("\\(Intercept\\)",nms)) } ##' @rdname downstream_methods ##' @rawNamespace if(getRversion() >= "3.6.0") { ##' S3method(car::Anova, glmmTMB) ##' } else { ##' export(Anova.glmmTMB) ##' } ##' @param vcov. variance-covariance matrix (usually extracted automatically) ##' @param test.statistic unused: only valid choice is "Chisq" (i.e., Wald chi-squared test) ##' @param singular.ok OK to do ANOVA with singular models (unused) ? ##' @param type type of test, \code{"II"}, \code{"III"}, \code{2}, or \code{3}. Roman numerals are equivalent to the corresponding Arabic numerals. See \code{\link[car]{Anova}} for details. Anova.glmmTMB <- function (mod, type = c("II", "III", 2, 3), test.statistic = c("Chisq","F"), component="cond", vcov. = vcov(mod)[[component]], singular.ok, ...) { ff <- fixef(mod)[[component]] if (trivialFixef(names(ff),component)) { stop(sprintf("trivial fixed effect for component %s: can't compute Anova table", sQuote(component))) } test.statistic <- match.arg(test.statistic) if (test.statistic=="F") { stop("F tests currently unavailable") } if (is.function(vcov.)) vcov. <- vcov.(mod) type <- as.character(type) type <- match.arg(type) if (missing(singular.ok)) singular.ok <- type == "2" || type == "II" afun <- switch(type, `2` = , II = Anova.II.glmmTMB, `3` = , III = Anova.III.glmmTMB) afun(mod, vcov., test=test.statistic, singular.ok = singular.ok, component = component) } ## defined as a function, not a method, so we can hand the object ## off to car::linearHypothesis.default (not exported) linearHypothesis_glmmTMB <- function (model, hypothesis.matrix, rhs = NULL, test = c("Chisq", "F"), vcov. = NULL, singular.ok = FALSE, verbose = FALSE, coef. = NULL, component="cond", ...) { ## what's the least ugly way to do this? ## match.call? test <- match.arg(test) ## call linearHypothesis.default (not exported) if (!requireNamespace("car")) { stop("please install (if necessary) and load the car package") } if (utils::packageVersion("car")<"3.0.6") { stop("please install a more recent version of the car package (>= 3.0.6)") } car::linearHypothesis(model=model, hypothesis.matrix=hypothesis.matrix, rhs=rhs, test=test, vcov. = vcov(model)[[component]], singular.ok = FALSE, verbose = verbose, coef. = fixef(model)[[component]], ...) } Anova.II.glmmTMB <- function(mod, vcov., singular.ok=TRUE, test="Chisq", component="cond", ...){ ## would feel cleaner to have this external, but it uses ## lots of variable from the function environment ... hyp.term <- function(term) { which.term <- which(term==names) subs.term <- which(assign==which.term) relatives <- relatives(term, names, fac) subs.relatives <- NULL for (relative in relatives) subs.relatives <- c(subs.relatives, which(assign==relative)) hyp.matrix.1 <- I.p[subs.relatives,,drop=FALSE] hyp.matrix.1 <- hyp.matrix.1[, not.aliased, drop=FALSE] hyp.matrix.2 <- I.p[c(subs.relatives,subs.term),,drop=FALSE] hyp.matrix.2 <- hyp.matrix.2[, not.aliased, drop=FALSE] hyp.matrix.term <- if (nrow(hyp.matrix.1) == 0) { hyp.matrix.2 } else { t(ConjComp(t(hyp.matrix.1), t(hyp.matrix.2), vcov.)) } hyp.matrix.term <- hyp.matrix.term[!apply(hyp.matrix.term, 1, function(x) all(x == 0)), , drop=FALSE] if (nrow(hyp.matrix.term) == 0) return(c(statistic=NA, df=0)) hyp <- linearHypothesis_glmmTMB(mod, hyp.matrix.term, vcov.=vcov., singular.ok=singular.ok, test=test, component=component, ...) if (test == "Chisq") return(c(statistic=hyp$Chisq[2], df=hyp$Df[2])) else return(c(statistic=hyp$F[2], df=hyp$Df[2], res.df=hyp$Res.Df[2])) } ## hyp.term() ## may be irrelevant, glmmTMB doesn't currently handle aliased terms? not.aliased <- !is.na(fixef(mod)[[component]]) if (!singular.ok && !all(not.aliased)) stop("there are aliased coefficients in the model") fac <- attr(terms(mod, component=component), "factors") intercept <- has.intercept(mod) p <- length(fixef(mod)[[component]]) I.p <- diag(p) if (!missing(vcov.)){ vcov. <- vcov(mod, complete=FALSE)[[component]] } assign <- attr(model.matrix(mod, component=component), "assign") assign[!not.aliased] <- NA names <- term.names.default(mod, component=component) if (intercept) names <- names[-1] n.terms <- length(names) p <- teststat <- df <- res.df <- rep(0, n.terms) for (i in 1:n.terms){ hyp <- hyp.term(names[i]) teststat[i] <- abs(hyp["statistic"]) df[i] <- abs(hyp["df"]) res.df[i] <- hyp["res.df"] p[i] <- pchisq(teststat[i], df[i], lower.tail=FALSE) } result <- data.frame(teststat, df, p) row.names(result) <- names names(result) <- c ("Chisq", "Df", "Pr(>Chisq)") class(result) <- c("anova", "data.frame") attr(result, "heading") <- c("Analysis of Deviance Table (Type II Wald chisquare tests)\n", paste("Response:", responseName.default(mod))) return(result) } Anova.III.glmmTMB <- function(mod, vcov., singular.ok=FALSE, test="Chisq", component="cond", ...){ intercept <- has.intercept(mod) p <- length(fixef(mod)[[component]]) I.p <- diag(p) names <- term.names.default(mod) n.terms <- length(names) assign <- attr(model.matrix(mod), "assign") p <- teststat <- df <- res.df <- rep(0, n.terms) if (intercept) df[1] <- 1 not.aliased <- !is.na(fixef(mod)[[component]]) if (!singular.ok && !all(not.aliased)) stop("there are aliased coefficients in the model") if (!missing(vcov.)){ vcov. <- vcov(mod, complete=FALSE)[[component]] } for (term in 1:n.terms){ subs <- which(assign == term - intercept) hyp.matrix <- I.p[subs,,drop=FALSE] hyp.matrix <- hyp.matrix[, not.aliased, drop=FALSE] hyp.matrix <- hyp.matrix[!apply(hyp.matrix, 1, function(x) all(x == 0)), , drop=FALSE] if (nrow(hyp.matrix) == 0){ teststat[term] <- NA df[term] <- 0 p[term] <- NA } else { hyp <- linearHypothesis_glmmTMB(mod, hyp.matrix, test=test, vcov.=vcov., singular.ok=singular.ok, ...) teststat[term] <- hyp$Chisq[2] df[term] <- abs(hyp$Df[2]) p[term] <- pchisq(teststat[term], df[term], lower.tail=FALSE) } result <- data.frame(teststat, df, p) row.names(result) <- names names(result) <- c ("Chisq", "Df", "Pr(>Chisq)") class(result) <- c("anova", "data.frame") attr(result, "heading") <- c("Analysis of Deviance Table (Type III Wald chisquare tests)\n", paste("Response:", responseName.default(mod))) } result } glmmTMB/R/family.R0000644000176200001440000003323213614324717013371 0ustar liggesusers## FIXME: I would like to use the following function instead of repeating ## the pattern, but I'm worried that lazy evaluation of arguments will ## cause all kinds of trouble family_factory <- function(default_link,family,variance) { f <- function(link=default_link) { r <- list(family=family,link=link,variance=variance) r <- c(r,make.link(link)) class(r) <- "family" return(r) } return(f) } ## suppress code warnings for nbinom2; can't use .Theta <- NULL trick here ... utils::globalVariables(".Theta") ## attempt to guess whether calling function has been called from glm.fit ... in_glm_fit <- function() { vars <- ls(envir=parent.frame(2)) all(c("coefold","control","EMPTY","good","nvars") %in% vars) } make_family <- function(x,link) { x <- c(x,list(link=link),make.link(link)) ## stubs for Effect.default/glm.fit if (is.null(x$aic)) { x <- c(x,list(aic=function(...) NA_real_)) } if (is.null(x$initialize)) { ## should handle log-links adequately x <- c(x,list(initialize=expression({mustart <- y+0.1}))) } if (is.null(x$dev.resids)) { ## can't return NA, glm.fit is unhappy x <- c(x,list(dev.resids=function(y,mu,wt) { rep(0,length(y)) })) } class(x) <- "family" return(x) } ## even better (?) would be to have a standalone list including ## name, default link, variance function, (optionally) initialize ## for each family ##' Family functions for glmmTMB ##' ##' ##' ##' @aliases family_glmmTMB ##' @param link (character) link function for the conditional mean ("log", "logit", "probit", "inverse", "cloglog", "identity", or "sqrt") ##' @return returns a list with (at least) components ##' \item{family}{length-1 character vector giving the family name} ##' \item{link}{length-1 character vector specifying the link function} ##' \item{variance}{a function of either 1 (mean) or 2 (mean and dispersion ##' parameter) arguments giving a value proportional to the ##' predicted variance (scaled by \code{sigma(.)}) ##' } ##' @details ##' If specified, the dispersion model uses a log link. Denoting the variance as \eqn{V}, the dispersion parameter ##' as \eqn{\phi=\exp(\eta)}{phi=exp(eta)} (where \eqn{\eta}{eta} is the linear predictor from the dispersion model), ##' and the predicted mean as \eqn{\mu}{mu}: ##' \describe{ ##' \item{gaussian}{(from base R): constant \eqn{V=\phi}{V=phi}} ##' \item{Gamma}{(from base R) phi is the shape parameter. \eqn{V=\mu\phi}{V=mu*phi}} ##' \item{ziGamma}{a modified version of \code{Gamma} that skips checks for zero values, allowing it to be used to fit hurdle-Gamma models} ##' \item{nbinom2}{Negative binomial distribution: quadratic parameterization (Hardin & Hilbe 2007). \eqn{V=\mu(1+\mu/\phi) = \mu+\mu^2/\phi}{V=mu*(1+mu/phi) = mu+mu^2/phi}.} ##' \item{nbinom1}{Negative binomial distribution: linear parameterization (Hardin & Hilbe 2007). \eqn{V=\mu(1+\phi)}{V=mu*(1+phi)}} ##' \item{compois}{Conway-Maxwell Poisson distribution: parameterized with the exact mean (Huang 2017), which differs from the parameterization used in the \pkg{COMPoissonReg} package (Sellers & Shmueli 2010, Sellers & Lotze 2015). \eqn{V=\mu\phi}{V=mu*phi}.} ##' \item{genpois}{Generalized Poisson distribution (Consul & Famoye 1992). \eqn{V=\mu\exp(\eta)}{V=mu*exp(eta)}. (Note that Consul & Famoye (1992) define \eqn{\phi}{phi} differently.)} ##' \item{beta}{Beta distribution: parameterization of Ferrari and Cribari-Neto (2004) ##' and the \pkg{betareg} package (Cribari-Neto and Zeileis 2010); \eqn{V=\mu(1-\mu)\phi}{V=mu*(1-mu)*phi}} ##' \item{betabinomial}{Beta-binomial distribution: parameterized according to Morris (1997). \eqn{V=\mu(1-\mu)(n(\phi+n)/(\phi+1))}{V=mu*(1-mu)*(n*(phi+n)/(phi+1))}} ##' \item{tweedie}{Tweedie distribution: \eqn{V=\phi\mu^p}{V=phi*mu^p}. The power parameter is restricted to the interval \eqn{1= 1)) { stop("y values must be 0 <= y < 1") } } else { if (any(y <= 0 | y >= 1)) stop("y values must be 0 < y < 1") } mustart <- y })) return(make_family(r,link)) } ## fixme: better name? #' @rdname nbinom2 #' @export ## variance= (Wikipedia) ## n*alpha*beta*( alpha + beta + n )/ ((alpha+beta)^2*(alpha+beta+1)) ## alpha = p*theta ## beta = (1-p)*theta ## -> n*p*(1-p)*theta^2*(theta+n)/(theta^2*(theta+1)) ## = n*p*(1-p)*(theta+n)/(theta+1) ## *scaled* variance (dependence on mu only) is still just mu*(1-mu); ## scaling is n*(theta+n)/(theta+1) (vs. simply n for the binomial) betabinomial <- function(link="logit") { r <- list(family="betabinomial", variance=function(mu,phi) { mu*(1-mu) }, initialize = binomial()$initialize) return(make_family(r,link)) } #' @rdname nbinom2 #' @export tweedie <- function(link="log") { r <- list(family="tweedie", variance=function(mu,phi,p) { stop("variance for tweedie family not yet implemented") }) return(make_family(r,link)) } ## t not yet implemented in ## t_family <- function(link="identity") { ## ## FIXME: right now t behaves just like gaussian(); variance() ## ## returns a value *proportional* to the variance ## r <- list(family="t",link=link, ## variance=function(mu) { ## rep.int(1,length(mu)) ## }) ## } #' List model options that glmmTMB knows about #' #' @note these are all the options that are \emph{defined} internally; they have not necessarily all been \emph{implemented} (FIXME!) #' @param what (character) which type of model structure to report on #' ("all","family","link","covstruct") #' @param check (logical) do brute-force checking to test whether families are really implemented (only available for \code{what="family"}) #' @return if \code{check==FALSE}, returns a vector of the names (or a list of name vectors) of allowable entries; if \code{check==TRUE}, returns a logical vector of working families #' #' @export getCapabilities <- function(what="all",check=FALSE) { if (!check) { switch(what, all=lapply(list(family=.valid_family,link=.valid_link, covstruct=.valid_covstruct),names), family=names(.valid_family), link=names(.valid_link), covstruct=names(.valid_covstruct), stop(sprintf("unknown option %s",what))) } else { ## run dummy models to see if we get a family-not-implemented error if (what!="family") stop("'check' option only available for families") families <- names(.valid_family) family_OK <- setNames(rep(TRUE,length(.valid_family)),families) y <- 1:3 ## dummy for (f in families) { tt1 <- utils::capture.output(tt0 <- suppressMessages(suppressWarnings( try(glmmTMB(y~1, family=list(family=f,link="identity")), silent=TRUE)))) family_OK[f] <- !(inherits(tt0,"try-error") && grepl("Family not implemented!",tt0)) } return(family_OK) } } #' @export #' @rdname nbinom2 ziGamma <- function(link="inverse") { g <- stats::Gamma(link=link) ## stats::Gamma does clever deparsing stuff ... need to work around it ... if (is.function(link)) { g$link <- deparse(substitute(link)) } else g$link <- link ## modify initialization to allow zero values in zero-inflated cases g$initialize <- expression({ if (exists("ziformula") && !ident(ziformula, ~0)) { if (any(y < 0)) stop("negative values not allowed for the 'Gamma' family with zero-inflation") } else { if (any(y <= 0)) stop("non-positive values not allowed for the 'Gamma' family") } n <- rep.int(1, nobs) mustart <- y }) return(g) } glmmTMB/R/enum.R0000644000176200001440000000144513614324717013055 0ustar liggesusers## Auto generated - do not edit by hand .valid_link <- c( log = 0, logit = 1, probit = 2, inverse = 3, cloglog = 4, identity = 5, sqrt = 6 ) .valid_family <- c( gaussian = 0, binomial = 100, betabinomial =101, beta =200, Gamma =300, poisson =400, truncated_poisson =401, genpois =402, compois =403, truncated_genpois =404, truncated_compois =405, nbinom1 =500, nbinom2 =501, truncated_nbinom1 =502, truncated_nbinom2 =503, t =600, tweedie = 700 ) .valid_covstruct <- c( diag = 0, us = 1, cs = 2, ar1 = 3, ou = 4, exp = 5, gau = 6, mat = 7, toep = 8 ) .valid_zipredictcode <- c( corrected = 0, uncorrected = 1, prob = 2, disp = 3 ) glmmTMB/R/lme4_utils_temp.R0000644000176200001440000000154013614324717015213 0ustar liggesusers#### COPIED from lme4, until next version of lme4 (>= 1.18-1.9000) gets to CRAN asDf0 <- function(x,nx,id=FALSE) { xt <- x[[nx]] ss <- utils::stack(xt) ss$ind <- factor(as.character(ss$ind), levels = colnames(xt)) ss$.nn <- rep.int(stats::reorder(factor(rownames(xt)), xt[[1]], FUN = mean,sort = sort), ncol(xt)) ## allow 'postVar' *or* 'condVar' names pv <- attr(xt,"postVar") if (is.null(pv)) { pv <- attr(xt,"condVar") } if (!is.null(pv)) { tmpfun <- function(pvi) { unlist(lapply(1:nrow(pvi), function(i) sqrt(pvi[i, i, ]))) } if (!is.list(pv)) { ss$se <- tmpfun(pv) } else { ## rely on ordering when unpacking! ss$se <- unlist(lapply(pv,tmpfun)) } } if (id) ss$id <- nx return(ss) } glmmTMB/R/emmeans.R0000644000176200001440000001043213616054060013523 0ustar liggesusers## methods for extending emmeans to handle glmmTMB objects ## NOTE: methods are dynamically exported by emmeans utility -- see code in zzz.R ##' Downstream methods for glmmTMB objects ##' ##' Methods have been written that allow \code{glmmTMB} objects to be used with ##' several downstream packages that enable different forms of inference. ##' In particular, ##' \itemize{ ##' \item \code{car::Anova} constructs type-II and type-III Anova tables ##' for the fixed effect parameters of the conditional model (this might work with the ##' fixed effects of the zero-inflation or dispersion models, but has not been tested) ##' \item the \code{effects} package computes graphical tabular effect displays ##' (again, for the fixed effects of the conditional component) ##' \item the \code{emmeans} package computes estimated marginal means (aka least-squares means) ##' for the fixed effects of the conditional component ##' } ##' @rdname downstream_methods ##' @param mod a glmmTMB model ##' @param object a glmmTMB model ##' @param trms The \code{terms} component of \code{object} (typically with the ##' response deleted, e.g. via \code{\link{delete.response}} ##' @param xlev Named list of factor levels (\emph{excluding} ones coerced to ##' factors in the model formula) ##' @param \dots Additional parameters that may be supported by the method. ##' @param grid A \code{data.frame} (provided by \code{ref_grid}) containing the ##' predictor settings needed in the reference grid ##' @details While the examples below are disabled for earlier versions of ##' R, they may still work; it may be necessary to refer to private ##' versions of methods, e.g. \code{glmmTMB:::Anova.glmmTMB(model, ...)}. ##' @examples ##' warp.lm <- glmmTMB(breaks ~ wool * tension, data = warpbreaks) ##' if (require(emmeans)) { ##' emmeans (warp.lm, poly ~ tension | wool) ##' } ##' if (getRversion() >= "3.6.0") { ##' if (require(car)) { ##' Anova(warp.lm,type="III") ##' } ##' if (require(effects) ##' plot(allEffects(warp.lm)) ##' } ##' } ## recover_data method -- DO NOT export -- see zzz.R #' @importFrom stats delete.response recover_data.glmmTMB <- function(object, ...) { fcall <- getCall(object) if (!requireNamespace("emmeans")) stop("please install (if necessary) and load the emmeans package") emmeans::recover_data(fcall,delete.response(terms(object)), attr(model.frame(object),"na.action"), ...) } ## emm_basis method -- Dynamically exported, see zzz.R #' @rdname downstream_methods #' @aliases downstream_methods #' @param component which component of the model to compute emmeans for (conditional ("cond"), zero-inflation ("zi"), or dispersion ("disp")) emm_basis.glmmTMB <- function (object, trms, xlev, grid, component="cond", ...) { ## Not needed anymore? ## if (component != "cond") warning("only tested for conditional component") V <- as.matrix(vcov(object)[[component]]) misc = list() if (family(object)$family=="gaussian") { dfargs = list(df = df.residual(object)) dffun = function(k, dfargs) dfargs$df } else { dffun = function(k, dfargs) Inf dfargs = list() } fam = switch(component, cond = family(object), zi = list(link = "logit"), disp = list(link = "log")) misc = emmeans::.std.link.labels(fam, misc) ## (used to populate the reminder of response scale) contrasts = attr(model.matrix(object), "contrasts") ## keep only variables found in conditional fixed effects contrasts = contrasts[names(contrasts) %in% all.vars(terms(object))] m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = contrasts) bhat = fixef(object)[[component]] if (length(bhat) < ncol(X)) { kept = match(names(bhat), dimnames(X)[[2]]) bhat = NA * X[1, ] bhat[kept] = fixef(object)[[component]] modmat = model.matrix(trms, model.frame(object), contrasts.arg = contrasts) nbasis = estimability::nonest.basis(modmat) } else { nbasis = estimability::all.estble } dfargs = list(df = df.residual(object)) dffun = function(k, dfargs) dfargs$df list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun, dfargs = dfargs, misc = misc) } glmmTMB/R/profile.R0000644000176200001440000001702013614324717013545 0ustar liggesusers#' Compute likelihood profiles for a fitted model #' #' @inheritParams confint.glmmTMB #' @param fitted a fitted \code{glmmTMB} object #' @param parm which parameters to profile, specified #' \itemize{ #' \item by index (position) #' \item by name (matching the row/column names of \code{vcov(object,full=TRUE)}) #' \item as \code{"theta_"} (random-effects variance-covariance parameters) or \code{"beta_"} (conditional and zero-inflation parameters) #' } #' @param level_max maximum confidence interval target for profile #' @param npts target number of points in (each half of) the profile (\emph{approximate}) #' @param stepfac initial step factor (fraction of estimated standard deviation) #' @param trace print tracing information? If \code{trace=FALSE} or 0, #' no tracing; if \code{trace=1}, print names of parameters currently #' being profiled; if \code{trace>1}, turn on tracing for the #' underlying \code{\link{tmbprofile}} function #' @param stderr standard errors to use as a scaling factor when picking step #' sizes to compute the profile; by default (if \code{stderr} is #' \code{NULL}, or \code{NA} for a particular element), #' uses the estimated (Wald) standard errors of the parameters #' @param ... additional arguments passed to \code{\link{tmbprofile}} #' @return An object of class \code{profile.glmmTMB}, which is also a #' data frame, with columns \code{.par} (parameter being profiled), #' \code{.focal} (value of focal parameter), value (negative log-likelihood). #' @importFrom stats profile #' @examples #' \dontrun{ #' m1 <- glmmTMB(count~ mined + (1|site), #' zi=~mined, family=poisson, data=Salamanders) #' salamander_prof1 <- profile(m1, parallel="multicore", #' ncpus=2, trace=1) #' ## testing #' salamander_prof1 <- profile(m1, trace=1,parm=1) #' salamander_prof1M <- profile(m1, trace=1,parm=1, npts = 4) #' salamander_prof2 <- profile(m1, parm="theta_") #' #' } #' salamander_prof1 <- readRDS(system.file("example_files","salamander_prof1.rds",package="glmmTMB")) #' if (require("ggplot2")) { #' ggplot(salamander_prof1,aes(.focal,sqrt(value))) + #' geom_point() + geom_line()+ #' facet_wrap(~.par,scale="free_x")+ #' geom_hline(yintercept=1.96,linetype=2) #' } #' @importFrom TMB tmbprofile #' @export profile.glmmTMB <- function(fitted, parm=NULL, level_max = 0.99, npts = 8, stepfac = 1/4, stderr = NULL, trace = FALSE, parallel = c("no", "multicore", "snow"), ncpus = getOption("profile.ncpus", 1L), cl = NULL, ...) { if (isREML(fitted)) stop("can't compute profiles for REML models at the moment (sorry)") plist <- parallel_default(parallel,ncpus) parallel <- plist$parallel do_parallel <- plist$do_parallel trace <- as.numeric(trace) ytol <- qchisq(level_max,1) ystep <- ytol/npts ## don't suppress sigma profiling (full=TRUE) parm <- getParms(parm, fitted, full=TRUE) ## only need selected SDs sds <- sqrt(diag(vcov(fitted,full=TRUE))) sds <- sds[parm] if (!is.null(stderr)) { if (length(stderr) != length(sds)) { if (length(stderr)==1) { sds <- rep(stderr,length(sds)) } else { stop( sprintf("length(stderr) should equal 1 or number of parameters (%d)", length(parm))) } } else { sds[!is.na(stderr)] <- stderr[!is.na(stderr)] } } if (any(sds>1e3)) { warning("very large standard errors for parameters: ", names(sds)[sds>1e3]) } if (FALSE) { ## would like complete solution for assigning names to components ## (cond (fix/theta), zi (fix/theta), disp (fix/theta)) ## and matching order of parameters in object ... nn <- names(object$obj$par) drop_null <- function(x) x[lengths(x)>0] ff <- drop_null(fixef(object)) pn <- lapply(ff,names) nm <- unlist(mapply(paste,names(ff),pn,MoreArgs=list(sep="_"), SIMPLIFY=FALSE)) } FUN <- local({ function(p,s) { if (trace>0) cat("parameter",p,"\n") return(tmbprofile(fitted$obj, name=p, h=s/4, ytol=ytol, ystep=ystep, trace=(trace>1),...)) } }) if (do_parallel) { if (parallel == "multicore") { L <- parallel::mcmapply(FUN, parm, sds, mc.cores = ncpus, SIMPLIFY=FALSE) } else if (parallel=="snow") { if (is.null(cl)) { ## start cluster new_cl <- TRUE cl <- parallel::makePSOCKcluster(rep("localhost", ncpus)) } ## run L <- parallel::clusterMap(cl, FUN, parm, sds) if (new_cl) { ## stop cluster parallel::stopCluster(cl) } } } else { ## non-parallel L <- Map(FUN, parm, sds) } dfun <- function(x,n) { dd0 <- data.frame(n,x) names(dd0)[1:2] <- c(".par",".focal") dd0$value <- dd0$value - min(dd0$value,na.rm=TRUE) return(dd0) } dd <- Map(dfun, L, names(sds)) dd <- do.call(rbind,dd) class(dd) <- c("profile.glmmTMB","data.frame") return(dd) } #' @param object a fitted profile (\code{profile.glmmTMB}) object #' @param level confidence level #' @rdname profile.glmmTMB #' @details Fits natural splines separately to the points from each half of the profile for each #' specified parameter (i.e., values above and below the MLE), then finds the inverse functions #' to estimate the endpoints of the confidence interval #' @examples #' salamander_prof1 <- readRDS(system.file("example_files","salamander_prof1.rds",package="glmmTMB")) #' confint(salamander_prof1) #' confint(salamander_prof1,level=0.99) #' @importFrom splines interpSpline backSpline #' @export confint.profile.glmmTMB <- function(object, parm=NULL, level = 0.95, ...) { ## FIXME: lots of bulletproofing: ## non-monotonic values: error and/or linear interpolation ## non-monotonic spline, qval <- 0.5*qchisq(level,df=1) ci_fun <- function(dd) { dd <- dd[!duplicated(dd$.focal),] ## unique values: WHY?? hf <- with(dd,factor(.focal>.focal[which.min(value)], levels=c("FALSE","TRUE"))) halves <- split(dd,hf) res <- vapply(halves,ci_fun_half,numeric(1)) a <- (1 - level)/2 a <- c(a, 1 - a) names(res) <- format.perc(a, 3) return(res) } ## fit spline and invert for one half (lower, upper) of the profile ci_fun_half <- function(hh) { if (nrow(hh)==0) return(NA_real_) if (max(hh$value,na.rm=TRUE) treat as binary following glm ## yobs could be cbind(success, failure) ## yobs could be binary ## (yobs, weights) could be (proportions, size) ## On the C++ side 'yobs' must be the number of successes. if ( binomialType(family$family) ) { if (is.factor(yobs)) { ## following glm, ‘success’ is interpreted as the factor not ## having the first level (and hence usually of having the ## second level). yobs <- pmin(as.numeric(yobs)-1,1) size <- rep(1, nobs) } else { if(is.matrix(yobs)) { # yobs=cbind(success, failure) size <- yobs[,1] + yobs[,2] yobs <- yobs[,1] #successes } else { if(all(yobs %in% c(0,1))) { #binary size <- rep(1, nobs) } else { #proportions yobs <- weights * yobs size <- weights weights <- rep(1, nobs) } } } } if (is.null(size)) size <- numeric(0) data.tmb <- namedList( X = condList$X, Z = condList$Z, Xzi = ziList$X, Zzi = ziList$Z, Xd = dispList$X, ## Zdisp=dispList$Z, ## use c() on yobs, size to strip attributes such as 'AsIs' ## (which confuse MakeADFun) yobs = c(yobs), respCol, offset = condList$offset, zioffset = ziList$offset, doffset = dispList$offset, weights, size = c(size), ## information about random effects structure terms = condReStruc, termszi = ziReStruc, family = .valid_family[family$family], link = .valid_link[family$link], ziPredictCode = .valid_zipredictcode[ziPredictCode], doPredict = doPredict, whichPredict = whichPredict ) getVal <- function(obj, component) vapply(obj, function(x) x[[component]], numeric(1)) ## safer initialization for link functions that might give ## illegal predictions for certain families ## (sqrt() behaves weirdly for beta=0 ## [because inverse-link is symmetric around 0?] beta_init <- if (family$link %in% c("identity","inverse","sqrt")) 1 else 0 ## Extra family specific parameters numThetaFamily <- (family$family == "tweedie") rr0 <- function(n) { if (is.null(n)) numeric(0) else rep(0, n) } parameters <- with(data.tmb, list( beta = rep(beta_init, ncol(X)), betazi = rr0(ncol(Xzi)), b = rep(beta_init, ncol(Z)), bzi = rr0(ncol(Zzi)), betad = rep(betad_init, ncol(Xd)), theta = rr0(sum(getVal(condReStruc,"blockNumTheta"))), thetazi = rr0(sum(getVal(ziReStruc, "blockNumTheta"))), thetaf = rr0(numThetaFamily) )) for (p in names(start)) { if (!(p %in% names(parameters))) { stop(sprintf("unrecognized vector '%s' in %s",p,sQuote("start")), call. = FALSE) } if ((Lp <- length(parameters[[p]])) != (Ls <- length(start[[p]]))) { stop(sprintf("parameter vector length mismatch: in %s, length(%s)==%d, should be %d", sQuote("start"), p, Ls, Lp), call. = FALSE) } parameters[[p]] <- start[[p]] } randomArg <- c(if(ncol(data.tmb$Z) > 0) "b", if(ncol(data.tmb$Zzi) > 0) "bzi") ## REML if (REML) randomArg <- c(randomArg, "beta") dispformula <- dispformula.orig ## May have changed - restore return(namedList(data.tmb, parameters, mapArg, randomArg, grpVar, condList, ziList, dispList, condReStruc, ziReStruc, family, contrasts, respCol, allForm=namedList(combForm,formula,ziformula,dispformula), fr, se, call, verbose, REML, map)) } ##' Create X and random effect terms from formula ##' @param formula current formula, containing both fixed & random effects ##' @param mf matched call ##' @param fr full model frame ##' @param ranOK random effects allowed here? ##' @param type label for model type ##' @param contrasts a list of contrasts (see ?glmmTMB) ##' @return a list composed of ##' \item{X}{design matrix for fixed effects} ##' \item{Z}{design matrix for random effects} ##' \item{reTrms}{output from \code{\link{mkReTrms}} from \pkg{lme4}} ##' \item{offset}{offset vector, or vector of zeros if offset not specified} ##' ##' @importFrom stats model.matrix contrasts ##' @importFrom methods new ##' @importFrom lme4 findbars nobars getXReTrms <- function(formula, mf, fr, ranOK=TRUE, type="", contrasts) { ## fixed-effects model matrix X - ## remove random effect parts from formula: fixedform <- formula RHSForm(fixedform) <- nobars(RHSForm(fixedform)) terms <- NULL ## make sure it's empty in case we don't set it nobs <- nrow(fr) ## check for empty fixed form ## need to ignore environments when checking! ## ignore.environment= arg only works with closures idfun <- function(x,y) { environment(x) <- emptyenv() environment(y) <- emptyenv() return(identical(x,y)) } if (idfun(RHSForm(fixedform, as.form=TRUE), ~ 0) || idfun(RHSForm(fixedform, as.form=TRUE), ~ -1)) { X <- matrix(ncol=0, nrow=nobs) offset <- rep(0,nobs) } else { tt <- terms(fixedform) pv <- attr(mf$formula,"predvars") attr(tt, "predvars") <- fix_predvars(pv,tt) mf$formula <- tt terms_fixed <- terms(eval(mf,envir=environment(fixedform))) ## FIXME: make model matrix sparse?? i.e. Matrix:::sparse.model.matrix() X <- model.matrix(drop.special2(fixedform), fr, contrasts) ## will be 0-column matrix if fixed formula is empty offset <- rep(0,nobs) terms <- list(fixed=terms(terms_fixed)) if (inForm(fixedform,quote(offset))) { ## hate to match offset terms with model frame names ## via deparse, but since that what was presumably done ## internally to get the model frame names in the first place ... for (o in extractForm(fixedform,quote(offset))) { offset_nm <- deparse(o) ## don't think this will happen, but ... if (length(offset_nm)>1) { stop("trouble reconstructing offset name") } offset <- offset + fr[[offset_nm]] } } } ## ran-effects model frame (for predvars) ## important to COPY formula (and its environment)? ranform <- formula if (is.null(findbars(ranform))) { reTrms <- NULL Z <- new("dgCMatrix",Dim=c(as.integer(nobs),0L)) ## matrix(0, ncol=0, nrow=nobs) ss <- integer(0) } else { ## FIXME: check whether predvars are carried along correctly in terms if (!ranOK) stop("no random effects allowed in ", type, " term") RHSForm(ranform) <- subbars(RHSForm(reOnly(formula))) mf$formula <- ranform reTrms <- mkReTrms(findbars(RHSForm(formula)), fr, reorder.terms=FALSE) ss <- splitForm(formula) ss <- unlist(ss$reTrmClasses) Z <- t(reTrms$Zt) ## still sparse ... } ## if(is.null(rankX.chk <- control[["check.rankX"]])) ## rankX.chk <- eval(formals(lmerControl)[["check.rankX"]])[[1]] ## X <- chkRank.drop.cols(X, kind=rankX.chk, tol = 1e-7) ## if(is.null(scaleX.chk <- control[["check.scaleX"]])) ## scaleX.chk <- eval(formals(lmerControl)[["check.scaleX"]])[[1]] ## X <- checkScaleX(X, kind=scaleX.chk) ## list(fr = fr, X = X, reTrms = reTrms, family = family, formula = formula, ## wmsgs = c(Nlev = wmsgNlev, Zdims = wmsgZdims, Zrank = wmsgZrank)) namedList(X, Z, reTrms, ss, terms, offset) } ##' Extract grouping variables for random effect terms from a factor list ##' @title Get Grouping Variable ##' @param x "flist" object; a data frame of factors including an \code{assign} attribute ##' matching columns to random effect terms ##' @return character vector of grouping variables ##' @keywords internal ##' @examples ##' data(cbpp,package="lme4") ##' cbpp$obs <- factor(seq(nrow(cbpp))) ##' rt <- lme4::glFormula(cbind(size,incidence-size)~(1|herd)+(1|obs), ##' data=cbpp,family=binomial)$reTrms ##' getGrpVar(rt$flist) ##' @export getGrpVar <- function(x) { assign <- attr(x,"assign") names(x)[assign] } ##' Calculate random effect structure ##' Calculates number of random effects, number of parameters, ##' block size and number of blocks. Mostly for internal use. ##' @param reTrms random-effects terms list ##' @param ss a character string indicating a valid covariance structure. ##' Must be one of \code{names(glmmTMB:::.valid_covstruct)}; ##' default is to use an unstructured variance-covariance ##' matrix (\code{"us"}) for all blocks). ##' @return a list ##' \item{blockNumTheta}{number of variance covariance parameters per term} ##' \item{blockSize}{size (dimension) of one block} ##' \item{blockReps}{number of times the blocks are repeated (levels)} ##' \item{covCode}{structure code} ##' @examples ##' data(sleepstudy, package="lme4") ##' rt <- lme4::lFormula(Reaction~Days+(1|Subject)+(0+Days|Subject), ##' sleepstudy)$reTrms ##' rt2 <- lme4::lFormula(Reaction~Days+(Days|Subject), ##' sleepstudy)$reTrms ##' getReStruc(rt) ##' @importFrom stats setNames dist ##' @export getReStruc <- function(reTrms, ss=NULL) { ## information from ReTrms is contained in cnms, flist ## cnms: list of column-name vectors per term ## flist: data frame of grouping variables (factors) ## 'assign' attribute gives match between RE terms and factors if (is.null(reTrms)) { list() } else { ## Get info on sizes of RE components assign <- attr(reTrms$flist,"assign") nreps <- vapply(assign, function(i) length(levels(reTrms$flist[[i]])), 0) blksize <- diff(reTrms$Gp) / nreps ## figure out number of parameters from block size + structure type if (is.null(ss)) { ss <- rep("us",length(blksize)) } covCode <- .valid_covstruct[ss] parFun <- function(struc, blksize) { switch(as.character(struc), "0" = blksize, # diag "1" = blksize * (blksize+1) / 2, # us "2" = blksize + 1, # cs "3" = 2, # ar1 "4" = 2, # ou "5" = 2, # exp "6" = 2, # gau "7" = 3, # mat "8" = 2 * blksize - 1) # toep } blockNumTheta <- mapply(parFun, covCode, blksize, SIMPLIFY=FALSE) ans <- lapply( seq_along(ss), function(i) { tmp <- list(blockReps = nreps[i], blockSize = blksize[i], blockNumTheta = blockNumTheta[[i]], blockCode = covCode[i] ) if(ss[i] == "ar1"){ ## FIXME: Keep this warning ? if (any(reTrms$cnms[[i]][1] == "(Intercept)") ) warning("AR1 not meaningful with intercept") } if(ss[i] == "ou"){ times <- parseNumLevels(reTrms$cnms[[i]]) if (ncol(times) != 1) stop("'ou' structure is for 1D coordinates only.") if (is.unsorted(times, strictly=TRUE)) stop("'ou' is for strictly sorted times only.") tmp$times <- drop(times) } if(ss[i] %in% c("exp", "gau", "mat")){ coords <- parseNumLevels(reTrms$cnms[[i]]) tmp$dist <- as.matrix( dist(coords) ) } tmp }) setNames(ans, names(reTrms$Ztlist)) } } .noDispersionFamilies <- c("binomial", "poisson", "truncated_poisson") ## BMB: why not just sigma(x)!=1.0 ... ? (redundant with sigma.glmmTMB) usesDispersion <- function(x) { is.na(match(x, .noDispersionFamilies)) ## !x %in% .noDispersionFamilies } .classicDispersionFamilies <- c("gaussian","Gamma","t") ## select only desired pieces from results of getXReTrms stripReTrms <- function(xrt, whichReTrms = c("cnms","flist"), which="terms") { c(xrt$reTrms[whichReTrms],setNames(xrt[which],which)) } #.okWeightFamilies <- c("binomial", "betabinomial") okWeights <- function(x) { TRUE #!is.na(match(x, .okWeightFamilies)) ## x %in% .okWeightFamilies } ## Families for which binomial()$initialize is used .binomialFamilies <- c("binomial", "betabinomial") binomialType <- function(x) { !is.na(match(x, .binomialFamilies)) } ##' Fit Models with TMB ##' ##' Fit a generalized linear mixed model (GLMM) using Template Model Builder (TMB). ##' @param formula combined fixed and random effects formula, following lme4 syntax. ##' @param data optional data frame containing model variables. ##' @param family a family function, a character string naming a family function, or the result of a call to a family function (variance/link function) information. See \code{\link{family}} for a generic discussion of families or \code{\link{family_glmmTMB}} for details of \code{glmmTMB}-specific families. ##' @param ziformula a \emph{one-sided} (i.e., no response variable) formula for zero-inflation combining fixed and random effects: the default \code{~0} specifies no zero-inflation. Specifying \code{~.} sets the zero-inflation formula identical to the right-hand side of \code{formula} (i.e., the conditional effects formula); terms can also be added or subtracted. \strong{When using \code{~.} as the zero-inflation formula in models where the conditional effects formula contains an offset term, the offset term will automatically be dropped}. The zero-inflation model uses a logit link. ##' @param dispformula a \emph{one-sided} formula for dispersion containing only fixed effects: the default \code{~1} specifies the standard dispersion given any family. The argument is ignored for families that do not have a dispersion parameter. For an explanation of the dispersion parameter for each family, see \code{\link{sigma}}. The dispersion model uses a log link. In Gaussian mixed models, \code{dispformula=~0} fixes the residual variance to be 0 (actually a small non-zero value: at present it is set to \code{sqrt(.Machine$double.eps)}), forcing variance into the random effects. ##' @param weights weights, as in \code{glm}. Not automatically scaled to have sum 1. ##' @param offset offset for conditional model (only). ##' @param contrasts an optional list, e.g., \code{list(fac1="contr.sum")}. See the \code{contrasts.arg} of \code{\link{model.matrix.default}}. ##' @param na.action how to handle missing values, see \code{\link{na.action}} and \code{\link{model.frame}}. From \code{\link{lm}}: \dQuote{The default is set by the \code{\link{na.action}} setting of \code{\link{options}}, and is \code{\link{na.fail}} if that is unset. The \sQuote{factory-fresh} default is \code{\link{na.omit}}.} ##' @param se whether to return standard errors. ##' @param verbose whether progress indication should be printed to the console. ##' @param doFit whether to fit the full model, or (if FALSE) return the preprocessed data and parameter objects, without fitting the model. ##' @param control control parameters, see \code{\link{glmmTMBControl}}. ##' @param REML whether to use REML estimation rather than maximum likelihood. ##' @param start starting values, expressed as a list with possible components \code{beta}, \code{betazi}, \code{betad} (fixed-effect parameters for conditional, zero-inflation, dispersion models); \code{b}, \code{bzi} (conditional modes for conditional and zero-inflation models); \code{theta}, \code{thetazi} (random-effect parameters, on the standard deviation/Cholesky scale, for conditional and z-i models); \code{thetaf} (extra family parameters, e.g., shape for Tweedie models). ##' @param map a list specifying which parameter values should be fixed to a constant value rather than estimated. \code{map} should be a named list containing factors corresponding to a subset of the internal parameter names (see \code{start} parameter). Distinct factor values are fitted as separate parameter values, \code{NA} values are held fixed: e.g., \code{map=list(beta=factor(c(1,2,3,NA)))} would fit the first three fixed-effect parameters of the conditional model and fix the fourth parameter to its starting value. In general, users will probably want to use \code{start} to specify non-default starting values for fixed parameters. See \code{\link[TMB]{MakeADFun}} for more details. ##' @importFrom stats gaussian binomial poisson nlminb as.formula terms model.weights ##' @importFrom lme4 subbars findbars mkReTrms nobars ##' @importFrom Matrix t ##' @importFrom TMB MakeADFun sdreport ##' @details ##' Binomial models with more than one trial (i.e., not binary/Bernoulli) can either be specified in the form \code{prob ~ ..., weights = N}, or in the more typical two-column matrix \code{cbind(successes,failures)~...} form. ##' ##' Behavior of \code{REML=TRUE} for Gaussian responses matches \code{lme4::lmer}. It may also be useful in some cases with non-Gaussian responses (Millar 2011). Simulations should be done first to verify. ##' ##' Because the \code{\link{df.residual}} method for \code{glmmTMB} currently counts the dispersion parameter, one would need to multiply by \code{sqrt(nobs(fit) / (1+df.residual(fit)))} when comparing with \code{lm}. ##' ##' By default, vector-valued random effects are fitted with unstructured (general positive definite) variance-covariance matrices. Structured variance-covariance matrices can be specified in the form \code{struc(terms|group)}, where \code{struc} is one of ##' \itemize{ ##' \item \code{diag} (diagonal, heterogeneous variance) ##' \item \code{ar1} (autoregressive order-1, homogeneous variance) ##' \item \code{cs} (compound symmetric, heterogeneous variance) ##' \item \code{ou} (* Ornstein-Uhlenbeck, homogeneous variance) ##' \item \code{exp} (* exponential autocorrelation) ##' \item \code{gau} (* Gaussian autocorrelation) ##' \item \code{mat} (* Matérn process correlation) ##' \item \code{toep} (* Toeplitz) ##' } ##' Structures marked with * are experimental/untested. ##' ##' For backward compatibility, the \code{family} argument can also be specified as a list comprising the name of the distribution and the link function (e.g. \code{list(family="binomial", link="logit")}). However, \strong{this alternative is now deprecated}; it produces a warning and will be removed at some point in the future. Furthermore, certain capabilities such as Pearson residuals or predictions on the data scale will only be possible if components such as \code{variance} and \code{linkfun} are present, see \code{\link{family}}. ##' ##' @note ##' For more information about the \pkg{glmmTMB} package, see Brooks et al. (2017) and the \code{vignette(package="glmmTMB")} collection. For the underlying \pkg{TMB} package that performs the model estimation, see Kristensen et al. (2016). ##' @references ##' Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Mächler, M. and Bolker, B. M. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. \emph{The R Journal}, \bold{9}(2), 378--400. ##' ##' Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H. and Bell, B. (2016). TMB: Automatic differentiation and Laplace approximation. \emph{Journal of Statistical Software}, \bold{70}, 1--21. ##' ##' Millar, R. B. (2011). \emph{Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB.} Wiley, New York. ##' @useDynLib glmmTMB ##' @importFrom stats update ##' @export ##' @examples ##' (m1 <- glmmTMB(count ~ mined + (1|site), ##' zi=~mined, ##' family=poisson, data=Salamanders)) ##' summary(m1) ##' \donttest{ ##' ## Zero-inflated negative binomial model ##' (m2 <- glmmTMB(count ~ spp + mined + (1|site), ##' zi=~spp + mined, ##' family=nbinom2, data=Salamanders)) ##' ##' ## Hurdle Poisson model ##' (m3 <- glmmTMB(count ~ spp + mined + (1|site), ##' zi=~spp + mined, ##' family=truncated_poisson, data=Salamanders)) ##' ##' ## Binomial model ##' data(cbpp, package="lme4") ##' (bovine <- glmmTMB(cbind(incidence, size-incidence) ~ period + (1|herd), ##' family=binomial, data=cbpp)) ##' ##' ## Dispersion model ##' sim1 <- function(nfac=40, nt=100, facsd=0.1, tsd=0.15, mu=0, residsd=1) ##' { ##' dat <- expand.grid(fac=factor(letters[1:nfac]), t=1:nt) ##' n <- nrow(dat) ##' dat$REfac <- rnorm(nfac, sd=facsd)[dat$fac] ##' dat$REt <- rnorm(nt, sd=tsd)[dat$t] ##' dat$x <- rnorm(n, mean=mu, sd=residsd) + dat$REfac + dat$REt ##' dat ##' } ##' set.seed(101) ##' d1 <- sim1(mu=100, residsd=10) ##' d2 <- sim1(mu=200, residsd=5) ##' d1$sd <- "ten" ##' d2$sd <- "five" ##' dat <- rbind(d1, d2) ##' m0 <- glmmTMB(x ~ sd + (1|t), dispformula=~sd, data=dat) ##' fixef(m0)$disp ##' c(log(5^2), log(10^2)-log(5^2)) # expected dispersion model coefficients ##' } ##' ##' ## Using 'map' to fix random-effects SD to 10 ##' m1_map <- update(m1, map=list(theta=factor(NA)), ##' start=list(theta=log(10))) ##' VarCorr(m1_map) glmmTMB <- function( formula, data = NULL, family = gaussian(), ziformula = ~0, dispformula= ~1, weights=NULL, offset=NULL, contrasts=NULL, na.action=na.fail, se=TRUE, verbose=FALSE, doFit=TRUE, control=glmmTMBControl(), REML=FALSE, start=NULL, map=NULL ) { ## edited copy-paste from glFormula ## glFormula <- function(formula, data=NULL, family = gaussian, ## subset, weights, na.action, offset, ## contrasts = NULL, mustart, etastart, ## control = glmerControl(), ...) { call <- mf <- mc <- match.call() if (is.character(family)) { if (family=="beta") { family <- "beta_family" warning("please use ",sQuote("beta_family()")," rather than ", sQuote("\"beta\"")," to specify a Beta-distributed response") } family <- get(family, mode = "function", envir = parent.frame()) } if (is.function(family)) { ## call family with no arguments family <- family() } ## FIXME: what is this doing? call to a function that's not really ## a family creation function? if (is.null(family$family)) { print(family) stop("'family' not recognized") } fnames <- names(family) if (!all(c("family","link") %in% fnames)) stop("'family' must contain at least 'family' and 'link' components") if (length(miss_comp <- setdiff(c("linkfun","variance"),fnames))>0) { warning("some components missing from ",sQuote("family"), ": downstream methods may fail") } if (grepl("^quasi", family$family)) stop('"quasi" families cannot be used in glmmTMB') ## extract family and link information from family object link <- family$link ## lme4 function for warning about unused arguments in ... ## ignoreArgs <- c("start","verbose","devFunOnly", ## "optimizer", "control", "nAGQ") ## l... <- list(...) ## l... <- l...[!names(l...) %in% ignoreArgs] ## do.call(checkArgs, c(list("glmer"), l...)) # substitute evaluated versions ## FIXME: denv leftover from lme4, not defined yet environment(formula) <- parent.frame() call$formula <- mc$formula <- formula ## add offset-specified-as-argument to formula as + offset(...) ## need evaluate offset within envi if (!is.null(eval(substitute(offset),data, enclos=environment(formula)))) { formula <- addForm0(formula,makeOp(substitute(offset),op=quote(offset))) } environment(ziformula) <- environment(formula) call$ziformula <- ziformula environment(dispformula) <- environment(formula) call$dispformula <- dispformula ## now work on evaluating model frame m <- match(c("data", "subset", "weights", "na.action", "offset"), names(mf), 0L) mf <- mf[c(1L, m)] mf$drop.unused.levels <- TRUE mf[[1]] <- as.name("model.frame") ## replace . in ziformula with conditional formula, ignoring offset if (inForm(ziformula,quote(.))) { ziformula <- update(RHSForm(drop.special2(formula),as.form=TRUE), ziformula) } ## want the model frame to contain the union of all variables ## used in any of the terms ## combine all formulas formList <- list(formula, ziformula, dispformula) for (i in seq_along(formList)) { f <- formList[[i]] ## abbreviate ## substitute "|" by "+"; drop specials f <- noSpecials(subbars(f),delete=FALSE) formList[[i]] <- f } combForm <- do.call(addForm,formList) environment(combForm) <- environment(formula) ## model.frame.default looks for these objects in the environment ## of the *formula* (see 'extras', which is anything passed in ...), ## so they have to be put there ... for (i in c("weights", "offset")) { if (!eval(bquote(missing(x=.(i))))) assign(i, get(i, parent.frame()), environment(combForm)) } mf$formula <- combForm fr <- eval(mf,envir=environment(formula),enclos=parent.frame()) ## FIXME: throw an error *or* convert character to factor ## convert character vectors to factor (defensive) ## fr <- factorize(fr.form, fr, char.only = TRUE) ## store full, original formula & offset ## attr(fr,"formula") <- combForm ## unnecessary? nobs <- nrow(fr) weights <- as.vector(model.weights(fr)) if(!is.null(weights) & !okWeights(family$family)) { stop("'weights' are not available for this family.") } if (is.null(weights)) weights <- rep(1,nobs) ## sanity checks (skipped!) ## wmsgNlev <- checkNlevels(reTrms$ flist, n=n, control, allow.n=TRUE) ## wmsgZdims <- checkZdims(reTrms$Ztlist, n=n, control, allow.n=TRUE) ## wmsgZrank <- checkZrank(reTrms$Zt, n=n, control, nonSmall=1e6, allow.n=TRUE) ## store info on location of response variable respCol <- attr(terms(fr), "response") names(respCol) <- names(fr)[respCol] ## extract response variable ## (name *must* be 'y' to match guts of family()$initialize y <- fr[,respCol] if (is.matrix(y)) { if ( ! binomialType(family$family) ) { stop("matrix-valued responses are not allowed") } } ## (1) transform 'y' appropriately for binomial models ## (2-column matrix, factor, logical -> numeric) ## (2) warn on non-integer values ## 'start' should *not* be (reset) to NULL here ## as far as we know (i.e. searching src/library/stats/R/family.R ## in the R source), 'start' is only referred to when family="gaussian" ## AND (inverse-link & any(y==0)) OR (log-link & any(y<=0)) ## and then only to check whether it's NULL or not ... etastart <- mustart <- NULL if (!is.null(family$initialize)) { local(eval(family$initialize)) ## 'local' so it checks but doesn't modify 'y' and 'weights' } if (grepl("^truncated", family$family) && (!is.factor(y) && any(y<0.001)) && (ziformula == ~0)) { stop(paste0("'", names(respCol), "'", " contains zeros (or values close to zero). ", "Zeros are compatible with a truncated distribution only when zero-inflation is added")) } if (grepl("(nbinom|pois)",family$family)) { ## see enum.R: this should cover nbinom1, nbinom2, ## poisson, genpois, compois, and the truncated variants ## binomial()$initialize already has its own check ## (shared by betabinomial) if (any(abs(y-round(y))>0.001)) { warning(sprintf("non-integer counts in a %s model", family$family)) } } TMBStruc <- mkTMBStruc(formula, ziformula, dispformula, combForm, mf, fr, yobs=y, respCol, weights, contrasts=contrasts, family=family, se=se, call=call, verbose=verbose, REML=REML, start=start, map=map) ## Allow for adaptive control parameters TMBStruc$control <- lapply(control, eval, envir=TMBStruc) ## short-circuit if (!doFit) return(TMBStruc) ## pack all the bits we will need for fitTMB res <- fitTMB(TMBStruc) return(res) } ##' Control parameters for glmmTMB optimization ##' @param optCtrl Passed as argument \code{control} to optimizer. Default value (if default \code{nlminb} optimizer is used): \code{list(iter.max=300, eval.max=400)} ##' @param optArgs additional arguments to be passed to optimizer function (e.g.: \code{list(method="BFGS")} when \code{optimizer=optim}) ##' @param profile Logical; Experimental option to improve speed and ##' robustness when a model has many fixed effects ##' @param collect Logical; Experimental option to improve speed by ##' recognizing duplicated observations. ##' @param parallel Numeric; Setting number of OpenMP threads to evaluate ##' the negative log-likelihood in parallel ##' @param optimizer Function to use in model fitting. See \code{Details} for required properties of this function. ##' @importFrom TMB openmp ##' @details ##' The general non-linear optimizer \code{nlminb} is used by ##' \code{\link{glmmTMB}} for parameter estimation. It may sometimes be ##' necessary to tweak some tolerances in order to make a model ##' converge. For instance, the warning \sQuote{iteration limit reached ##' without convergence} may be fixed by increasing the number of ##' iterations using something like ##' ##' \code{glmmTMBControl(optCtrl=list(iter.max=1e3,eval.max=1e3))}. ##' ##' The argument \code{profile} allows \code{glmmTMB} to use some special ##' properties of the optimization problem in order to speed up estimation ##' in cases with many fixed effects. Enable this option using ##' ##' \code{glmmTMBControl(profile=TRUE)}. ##' ##' Control parameters may depend on the model specification, because each ##' control component is evaluated inside \code{TMBStruc}, the output ##' of \code{mkTMBStruc}. To specify that \code{profile} should be ##' enabled for more than 5 fixed effects one can use ##' ##' \code{glmmTMBControl(profile=quote(length(parameters$beta)>=5))}. ##' ##' The \code{optimizer} argument can be any optimization (minimizing) function, provided that: ##' \itemize{ ##' \item the first three arguments, in order, are the starting values, objective function, and gradient function; ##' \item it also takes a \code{control} argument; ##' \item it returns a list with elements (at least) \code{convergence} (0 if convergence is successful) and \code{message} ##' } ##' @examples ##' ## fit with default (nlminb) and alternative (optim/BFGS) optimizer ##' m1 <- glmmTMB(count~ mined, family=poisson, data=Salamanders) ##' m1B <- update(m1, control=glmmTMBControl(optimizer=optim, ##' optArgs=list(method="BFGS"))) ##' ## estimates are *nearly* identical: ##' all.equal(fixef(m1), fixef(m1B)) ##' @export glmmTMBControl <- function(optCtrl=NULL, optArgs=list(), optimizer=nlminb, profile=FALSE, collect=FALSE, parallel = NULL) { if (is.null(optCtrl) && identical(optimizer,nlminb)) { optCtrl <- list(iter.max=300, eval.max=400) } ## Make sure that we specify at least one thread if (!is.null(parallel)) { if (is.na(parallel) || parallel < 1) { stop("Number of parallel threads must be a numeric >= 1") } parallel <- as.integer(parallel) } ## FIXME: Change defaults - add heuristic to decide if 'profile' is beneficial. ## Something like ## profile = (length(parameters$beta) >= 2) && ## (family$family != "tweedie") ## (TMB tweedie derivatives currently slow) namedList(optCtrl, profile, collect, parallel, optimizer, optArgs) } ##' collapse duplicated observations ##' @keywords internal ##' @importFrom stats runif xtabs .collectDuplicates <- function(data.tmb) { nm <- c("X", "Z", "Xzi", "Zzi", "Xd", "offset", "zioffset", "doffset", "yobs", "size"[length(data.tmb$size) > 0]) A <- do.call(cbind, data.tmb[nm]) ## Restore random seed on exit ## FIXME: Simplify ? seed <- .GlobalEnv$.Random.seed on.exit({ if (is.null(seed)) rm(".Random.seed", envir=.GlobalEnv) else .GlobalEnv$.Random.seed <- seed }) ## Generate hash code for data terms hash <- as.vector(A %*% runif(ncol(A))) hash <- format(hash, nsmall=20) keep <- !duplicated(hash) collect <- factor(hash, levels=hash[keep]) ## Check for collisions rownames(A) <- NULL A0 <- A[keep, , drop=FALSE] if( ! identical (A, A0[unclass(collect), ]) ) stop("Hash code collision !") ## Reduce nm <- c("X", "Z", "Xzi", "Zzi", "Xd") data.tmb[nm] <- lapply(data.tmb[nm], function(x) x[keep, , drop=FALSE]) nm <- c("offset", "zioffset", "doffset", "yobs", "size") data.tmb[nm] <- lapply(data.tmb[nm], function(x) x[keep]) ## Update weights data.tmb$weights <- xtabs(data.tmb$weights ~ collect) data.tmb } ## FIXME: export fitTMB? fitTMB <- function(TMBStruc) { control <- TMBStruc$control ## Assign OpenMP threads if (!is.null(control$parallel)) { n_orig <- TMB::openmp(NULL) ## will warn if OpenMP not supported ## only proceed farther if OpenMP *is* supported ... ## (avoid extra warnings) if (n_orig>0) { TMB::openmp(n = control$parallel) on.exit(TMB::openmp(n = n_orig)) } } if (control $ collect) { ## To avoid side-effects (e.g. nobs.glmmTMB), we restore ## original data (with duplicates) after fitting. data.tmb.old <- TMBStruc$data.tmb TMBStruc$data.tmb <- .collectDuplicates(TMBStruc$data.tmb) } ## avoid repetition; rely on environment for parameters optfun <- function() { with(obj, if( length(par) ) { do.call(control$optimizer, c(list(par, fn, gr, control = control $ optCtrl), control $ optArgs)) } else { list( par=par, objective=fn(par)) }) } if (control $ profile) { obj <- with(TMBStruc, MakeADFun(data.tmb, parameters, map = mapArg, random = randomArg, profile = "beta", silent = !verbose, DLL = "glmmTMB")) optTime <- system.time(fit <- optfun()) sdr <- sdreport(obj, getJointPrecision=TRUE) parnames <- names(obj$env$par) Q <- sdr$jointPrecision; dimnames(Q) <- list(parnames, parnames) whichNotRandom <- which( ! parnames %in% c("b", "bzi") ) Qm <- GMRFmarginal(Q, whichNotRandom) h <- as.matrix(Qm) ## Hessian of *all* (non-random) parameters TMBStruc$parameters <- obj$env$parList(fit$par, obj$env$last.par.best) ## Build object obj <- with(TMBStruc, MakeADFun(data.tmb, parameters, map = mapArg, random = randomArg, profile = NULL, silent = !verbose, DLL = "glmmTMB")) ## Run up to 5 Newton iterations with fixed (off-mode) hessian oldpar <- par <- obj$par; iter <- 0 ## FIXME: Make configurable ? max.newton.steps <- 5 newton.tol <- 1e-10 if (sdr$pdHess) { ## pdHess can be FALSE ## * Happens for boundary fits (e.g. dispersion close to 0 - see 'spline' example) ## * Option 1: Fall back to old method ## * Option 2: Skip Newton iterations for (iter in seq_len(max.newton.steps)) { g <- as.numeric( obj$gr(par) ) if (any(is.na(g)) || max(abs(g)) < newton.tol) break par <- par - solve(h, g) } } if (any(is.na(g))) { warning("a Newton step failed in profiling") par <- oldpar } fit$par <- par fit$objective <- obj$fn(par) fit$newton.steps <- iter } else { obj <- with(TMBStruc, MakeADFun(data.tmb, parameters, map = mapArg, random = randomArg, profile = NULL, silent = !verbose, DLL = "glmmTMB")) optTime <- system.time(fit <- optfun()) } fit$parfull <- obj$env$last.par.best ## This is in sync with fit$par fitted <- NULL if (TMBStruc$se) { if(control$profile) sdr <- sdreport(obj, hessian.fixed=h) else sdr <- sdreport(obj, getJointPrecision=TMBStruc$REML) ## FIXME: assign original rownames to fitted? } else { sdr <- NULL } if(!is.null(sdr$pdHess)) { if(!sdr$pdHess) { warning(paste0("Model convergence problem; ", "non-positive-definite Hessian matrix. ", "See vignette('troubleshooting')")) } else { eigval <- try(1/eigen(sdr$cov.fixed)$values, silent=TRUE) if( is(eigval, "try-error") || ( min(eigval) < .Machine$double.eps*10 ) ) { warning(paste0("Model convergence problem; ", "extreme or very small eigen values detected. ", "See vignette('troubleshooting')")) } } } if ( !is.null(fit$convergence) && fit$convergence != 0) warning("Model convergence problem; ", fit$message, ". ", "See vignette('troubleshooting')") if (control $ collect) { ## Undo changes made to the data TMBStruc$data.tmb <- data.tmb.old obj$env$data <- obj$env$dataSanitize(data.tmb.old) obj$retape() } modelInfo <- with(TMBStruc, namedList(nobs=nrow(data.tmb$X), respCol, grpVar, family, contrasts, ## FIXME:apply condList -> cond earlier? reTrms = lapply(list(cond=condList, zi=ziList), stripReTrms), terms = lapply(list(cond=condList, zi=ziList, disp=dispList), "[[", "terms"), reStruc = namedList(condReStruc, ziReStruc), allForm, REML, map)) ## FIXME: are we including obj and frame or not? ## may want model= argument as in lm() to exclude big stuff from the fit ## If we don't include obj we need to get the basic info out ## and provide a way to regenerate it as necessary ## If we don't include frame, then we may have difficulty ## with predict() in its current form ret <- structure(namedList(obj, fit, sdr, call=TMBStruc$call, frame=TMBStruc$fr, modelInfo, fitted), class = "glmmTMB") ## fill in dispersion parameters in environments of family variance ## functions, if possible (for glm/effects compatibility) ff <- ret$modelInfo$family ## family has variance component with extra parameters xvarpars <- (length(fv <- ff$variance)>0 && length(formals(fv))>1) nbfam <- ff$family=="negative.binomial" || grepl("nbinom",ff$family) if (nbfam || xvarpars) { theta <- exp(fit$parfull["betad"]) ## log link ## variance() and dev.resids() share an environment assign(".Theta", theta, environment(ret[["modelInfo"]][["family"]][["variance"]])) } return(ret) } ##' @importFrom stats AIC BIC llikAIC <- function(object) { llik <- logLik(object) AICstats <- c(AIC = AIC(llik), BIC = BIC(llik), logLik = c(llik), deviance = -2*llik, ## FIXME: df.resid = df.residual(object)) list(logLik = llik, AICtab = AICstats) } ## FIXME: export/import from lme4? ngrps <- function(object, ...) UseMethod("ngrps") ngrps.default <- function(object, ...) stop("Cannot extract the number of groups from this object") ngrps.glmmTMB <- function(object, ...) { res <- lapply(object$modelInfo$reTrms, function(x) vapply(x$flist, nlevels, 1)) ## FIXME: adjust reTrms names for consistency rather than hacking here names(res) <- gsub("List$","",names(res)) return(res) } ngrps.factor <- function(object, ...) nlevels(object) ##' @importFrom stats pnorm ##' @method summary glmmTMB ##' @export summary.glmmTMB <- function(object,...) { if (length(list(...)) > 0) { ## FIXME: need testing code warning("additional arguments ignored") } ## figure out useSc sig <- sigma(object) famL <- family(object) mkCoeftab <- function(coefs,vcov) { p <- length(coefs) coefs <- cbind("Estimate" = coefs, "Std. Error" = sqrt(diag(vcov))) if (p > 0) { coefs <- cbind(coefs, (cf3 <- coefs[,1]/coefs[,2]), deparse.level = 0) ## statType <- if (useSc) "t" else "z" statType <- "z" ## ??? should we provide Wald p-values??? coefs <- cbind(coefs, 2*pnorm(abs(cf3), lower.tail = FALSE)) colnames(coefs)[3:4] <- c(paste(statType, "value"), paste0("Pr(>|",statType,"|)")) } coefs } ff <- fixef(object) vv <- vcov(object) coefs <- setNames(lapply(names(ff), function(nm) if (trivialFixef(names(ff[[nm]]),nm)) NULL else mkCoeftab(ff[[nm]],vv[[nm]])), names(ff)) llAIC <- llikAIC(object) ## FIXME: You can't count on object@re@flist, ## nor compute VarCorr() unless is(re, "reTrms"): varcor <- VarCorr(object) # use S3 class for now structure(list(logLik = llAIC[["logLik"]], family = famL$family, link = famL$link, ngrps = ngrps(object), nobs = nobs(object), coefficients = coefs, sigma = sig, vcov = vcov(object), varcor = varcor, # and use formatVC(.) for printing. AICtab = llAIC[["AICtab"]], call = object$call ## residuals = residuals(object,"pearson",scaled = TRUE), ## fitMsgs = .merMod.msgs(object), ## optinfo = object@optinfo ), class = "summary.glmmTMB") } ## copied from lme4:::print.summary.merMod (makes use of ##' @importFrom lme4 .prt.family .prt.call .prt.resids .prt.VC .prt.grps ##' @importFrom stats printCoefmat ##' @method print summary.glmmTMB ##' @export print.summary.glmmTMB <- function(x, digits = max(3, getOption("digits") - 3), signif.stars = getOption("show.signif.stars"), ranef.comp = c("Variance", "Std.Dev."), show.resids = FALSE, ...) { .prt.family(x) .prt.call.glmmTMB(x$call); cat("\n") .prt.aictab(x$AICtab); cat("\n") if (show.resids) .prt.resids(x$residuals, digits = digits) if (any(whichRE <- !sapply(x$varcor,is.null))) { cat("Random effects:\n") for (nn in names(x$varcor[whichRE])) { cat("\n",cNames[[nn]],":\n",sep="") ## lme4:::.prt.VC is not quite what we want here print(formatVC(x$varcor[[nn]], digits = digits, comp 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Simulate from a fitted glmmTMB model

Mollie Brooks

2020-02-03

glmmTMB has the capability to simulate from a fitted model. These simulations resample random effects from their estimated distribution. In future versions of glmmTMB, it may be possible to condition on estimated random effects.

library(glmmTMB)
library(ggplot2); theme_set(theme_bw())

Fit a typical model:

data(Owls)
owls_nb1 <- glmmTMB(SiblingNegotiation ~ FoodTreatment*SexParent +
                             (1|Nest)+offset(log(BroodSize)),
                          family = nbinom1,
                          ziformula = ~1, data=Owls)

Then we can simulate from the fitted model with the simulate.glmmTMB function. It produces a list of simulated observation vectors, each of which is the same size as the original vector of observations. The default is to only simulate one vector (nsim=1) but we still return a list for consistency.

simo=simulate(owls_nb1, seed=1)
Simdat=Owls
Simdat$SiblingNegotiation=simo[[1]]
Simdat=transform(Simdat,  
            NegPerChick = SiblingNegotiation/BroodSize, 
            type="simulated")
Owls$type = "observed"  
Dat=rbind(Owls, Simdat)

Then we can plot the simulated data against the observed data to check if they are similar.

ggplot(Dat,  aes(NegPerChick, colour=type))+geom_density()+facet_grid(FoodTreatment~SexParent)

glmmTMB/inst/doc/parallel.Rmd0000644000176200001440000000704413614324717015550 0ustar liggesusers--- title: "Parallel optimization using glmmTMB" author: "Nafis Sadat" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{parallel optimization} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- A new, experimental feature of `glmmTMB` is the ability to parallelize the optimization process. This vignette shows an example and timing of a simple model fit with and without parallelizing across threads. If your OS supports OpenMP parallelization and R was installed using OpenMP, `glmmTMB` will automatically pick up the OpenMP flags from R's `Makevars` and compile the C++ model with OpenMP support. If the flag is not available, then the model will be compiled with serial optimization only. ```{r setup, include=FALSE, message=FALSE} library(knitr) ``` Load packages: ```{r libs,message=FALSE} library(glmmTMB) set.seed(1) nt <- min(parallel::detectCores(),5) ``` Simulate a dataset with large `N`: ```{r simulate1} N <- 3e5 xdata <- rnorm(N, 1, 2) ydata <- 0.3 + 0.4*xdata + rnorm(N, 0, 0.25) ``` First, we fit the model serially. We can pass the number of parallelizing process we want using the `parallel` parameter in `glmmTMBcontrol`: ```{r fit1} system.time( model1 <- glmmTMB(formula = ydata ~ 1 + xdata, control = glmmTMBControl(parallel = 1)) ) ``` Now, we fit the same model using five threads (or as many as possible - `r nt` in this case): ```{r fit2} system.time( model2 <- glmmTMB(formula = ydata ~ 1 + xdata, control = glmmTMBControl(parallel = nt)) ) ``` The speed-up is definitely more visible on models with a much larger number of observations, or in models with random effects. Here's an example where we have an IID Gaussian random effect. We first simulate the data with 200 groups (our random effect): ```{r simulate2} xdata <- rnorm(N, 1, 2) groups <- 200 data_use <- data.frame(obs = 1:N) data_use <- within(data_use, { group_var <- rep(seq(groups), times = nrow(data_use) / groups) group_intercept <- rnorm(groups, 0, 0.1)[group_var] xdata <- xdata ydata <- 0.3 + group_intercept + 0.5*xdata + rnorm(N, 0, 0.25) }) ``` We fit the random effect model, first with a single thread: ```{r fit3} (t_serial <- system.time( model3 <- glmmTMB(formula = ydata ~ 1 + xdata + (1 | group_var), data = data_use, control = glmmTMBControl(parallel = 1)) ) ) ``` Now we fit the same model, but using `r nt` threads. The speed-up is more noticeable with this model. ```{r fit4} (t_parallel <- system.time( update(model3, control = glmmTMBControl(parallel = nt)) ) ) ``` ## Notes on OpenMP support From [Writing R Extensions](https://cran.r-project.org/doc/manuals/r-devel/R-exts.html#OpenMP-support): > Apple builds of clang on macOS currently have no OpenMP support, but CRAN binary packages are built with a clang-based toolchain which supports OpenMP. http://www.openmp.org/resources/openmp-compilers-tools gives some idea of what compilers support what versions. > The performance of OpenMP varies substantially between platforms. The Windows implementation has substantial overheads, so is only beneficial if quite substantial tasks are run in parallel. Also, on Windows new threads are started with the default FPU control word, so computations done on OpenMP threads will not make use of extended-precision arithmetic which is the default for the main process. ## System information This report was built using `r nt` parallel threads (on a machine with a total of `r parallel::detectCores()` cores) ```{r SI} print(sessionInfo(), locale=FALSE) ``` glmmTMB/inst/doc/covstruct.rmd0000644000176200001440000004053413614324717016051 0ustar liggesusers--- title: "Covariance structures with glmmTMB" author: "Kasper Kristensen" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{random effect structures} %\VignettePackage{glmmTMB} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} params: EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true") --- ```{r setup, include=FALSE, message=FALSE} library(knitr) library(glmmTMB) library(MASS) ## for mvrnorm() library(TMB) ## for tmbprofile() ## devtools::install_github("kaskr/adcomp/TMB") ## get development version knitr::opts_chunk$set(echo = TRUE, eval=if (isTRUE(exists("params"))) params$EVAL else FALSE) ## turned off caching for now: got error in chunk 'fit.us.2' ## Error in retape() : ## Error when reading the variable: 'thetaf'. Please check data and parameters. ## In addition: Warning message: ## In retape() : Expected object. Got NULL. set.seed(1) ## run this in interactive session if you actually want to evaluate chunks ... ## Sys.setenv(NOT_CRAN="true") ``` This vignette demonstrates some of the covariance structures available in the `glmmTMB` package. Currently the available covariance structures are: | Covariance | Notation | Parameter count | Requirement | |----------------------------------|---------------|-----------------|-------------| | Heterogeneous unstructured | `us` | $n(n+1)/2$ | | | Heterogeneous Toeplitz | `toep` | $2n-1$ | | | Heterogeneous compound symmetry | `cs` | $n+1$ | | | Heterogeneous diagonal | `diag` | $n$ | | | AR(1) | `ar1` | $2$ | | | Ornstein–Uhlenbeck | `ou` | $2$ | Coordinates | | Spatial exponential | `exp` | $2$ | Coordinates | | Spatial Gaussian | `gau` | $2$ | Coordinates | | Spatial Matern | `mat` | $3$ | Coordinates | The word 'heterogeneous' refers to the marginal variances of the model. Beyond correlation parameters, a heterogeneous structure uses $n$ additional variance parameters where $n$ is the dimension. Some of the structures require temporal or spatial coordinates. We will show examples of this in a later section. ## The AR(1) covariance structure ### Demonstration on simulated data First, let's consider a simple time series model. Assume that our measurements $Y(t)$ are given at discrete times $t \in \{1,...,n\}$ by $$Y(t) = \mu + X(t) + \varepsilon(t)$$ where - $\mu$ is the mean value parameter. - $X(t)$ is a stationary AR(1) process, i.e. has covariance $cov(X(s), X(t)) = \sigma^2\exp(-\theta |t-s|)$. - $\varepsilon(t)$ is iid. $N(0,\sigma_0^2)$ measurement error. A simulation experiment is set up using the parameters | Description | Parameter | Value | |------------------------|---------------|-------| | Mean | $\mu$ | 0 | | Process variance | $\sigma^2$ | 1 | | Measurement variance | $\sigma_0^2$ | 1 | | One-step correlation | $e^{-\theta}$ | 0.7 | The following R-code draws a simulation based on these parameter values. For illustration purposes we consider a very short time series. ```{r sim1, eval=TRUE} n <- 6 ## Number of time points x <- mvrnorm(mu = rep(0,n), Sigma = .7 ^ as.matrix(dist(1:n)) ) ## Simulate the process using the MASS package y <- x + rnorm(n) ## Add measurement noise ``` In order to fit the model with `glmmTMB` we must first specify a time variable as a *factor*. The factor *levels* correspond to unit spaced time points. ```{r simtimes} times <- factor(1:n) levels(times) ``` We also need a grouping variable. In the current case there is only one time-series so the grouping is: ```{r simgroup} group <- factor(rep(1,n)) ``` We combine the data into a single data frame (not absolutely required, but good practice): ```{r simcomb} dat0 <- data.frame(y,times,group) ``` Now fit the model using ```{r fitar1, eval=FALSE} glmmTMB(y ~ ar1(times + 0 | group), data=dat0) ``` This formula notation follows that of the `lme4` package. - The left hand side of the bar `times + 0` corresponds to a design matrix $Z$ linking observation vector $y$ (rows) with a random effects vector $u$ (columns). - The distribution of $u$ is `ar1` (this is the only `glmmTMB` specific part of the formula). - The right hand side of the bar splits the above specification independently among groups. Each group has its own separate $u$ vector but shares the same parameters for the covariance structure. After running the model, we find the parameter estimates $\mu$ (intercept), $\sigma_0^2$ (dispersion), $\sigma$ (Std. Dev.) and $e^{-\theta}$ (First off-diagonal of "Corr") in the output: > FIXME: Try a longer time series when the print.VarCorr is fixed. ```{r ar0fit,echo=FALSE} glmmTMB(y ~ ar1(times + 0 | group), data=dat0) ``` ### Increasing the sample size A single time series of 6 time points is not sufficient to identify the parameters. We could either increase the length of the time series or increase the number of groups. We'll try the latter: ```{r simGroup} simGroup <- function(g, n=6, rho=0.7) { x <- mvrnorm(mu = rep(0,n), Sigma = rho ^ as.matrix(dist(1:n)) ) ## Simulate the process y <- x + rnorm(n) ## Add measurement noise times <- factor(1:n) group <- factor(rep(g,n)) data.frame(y, times, group) } simGroup(1) ``` Generate a dataset with 1000 groups: ```{r simGroup2} dat1 <- do.call("rbind", lapply(1:1000, simGroup) ) ``` And fitting the model on this larger dataset gives estimates close to the true values (AR standard deviation=1, residual (measurement) standard deviation=1, autocorrelation=0.7): ```{r fit.ar1} (fit.ar1 <- glmmTMB(y ~ ar1(times + 0 | group), data=dat1)) ``` ## The unstructured covariance We can try to fit an unstructured covariance to the previous dataset `dat`. For this case an unstructured covariance has `r (n*n-n)/2` correlation parameters and `r n` variance parameters. Adding $\sigma_0^2 I$ on top would cause a strict overparameterization, as these would be redundant with the diagonal elements in the covariance matrix. Hence, when fitting the model with `glmmTMB`, we have to disable the $\varepsilon$ term (the dispersion) by setting `dispformula=~0`: ```{r fit.us} fit.us <- glmmTMB(y ~ us(times + 0 | group), data=dat1, dispformula=~0) fit.us$sdr$pdHess ## Converged ? ``` The estimated variance and correlation parameters are: ```{r fit.us.vc} VarCorr(fit.us) ``` \newcommand{\textsub}{2}{#1_{{\text \small #2}}} The estimated correlation is approximately constant along diagonals (apparent Toeplitz structure) and we note that the first off-diagonal is now ca. half the true value (0.7) because the dispersion is effectively included in the estimated covariance matrix (i.e. $\rho' = \rho \textsub{\sigma^2}{AR}/(\textsub{\sigma^2}{AR} + \textsub{sigma^2}{meas})$). ## The Toeplitz structure The next natural step would be to reduce the number of parameters by collecting correlation parameters within the same off-diagonal. This amounts to `r (n-1)` correlation parameters and `r n` variance parameters. > FIXME: Explain why dispformula=~1 causes over-parameterization ```{r fit.toep} fit.toep <- glmmTMB(y ~ toep(times + 0 | group), data=dat1, dispformula=~0) fit.toep$sdr$pdHess ## Converged ? ``` The estimated variance and correlation parameters are: ```{r fit.toep.vc} (vc.toep <- VarCorr(fit.toep)) ``` The diagonal elements are all approximately equal to the true total variance ($\textsub{\sigma^2}{AR} + \textsub{sigma^2}{meas}$=2), and the off-diagonal elements are approximately equal to the expected value of 0.7/2=0.35. ```{r fit.toep.vc.diag} vc1 <- vc.toep$cond[[1]] ## first term of var-cov for RE of conditional model summary(diag(vc1)) summary(vc1[row(vc1)!=col(vc1)]) ``` We can get a *slightly* better estimate of the variance by using REML estimation (however, the estimate of the correlations seems to have gotten slightly worse): ```{r fit.toep.reml} fit.toep.reml <- update(fit.toep, REML=TRUE) vc1R <- VarCorr(fit.toep.reml)$cond[[1]] summary(diag(vc1R)) summary(vc1R[row(vc1R)!=col(vc1R)]) ``` ## Compound symmetry The compound symmetry structure collects all off-diagonal elements of the correlation matrix to one common value. > FIXME: Explain why dispformula=~1 causes over-parameterization ```{r fit.cs} fit.cs <- glmmTMB(y ~ cs(times + 0 | group), data=dat1, dispformula=~0) fit.cs$sdr$pdHess ## Converged ? ``` The estimated variance and correlation parameters are: ```{r fit.cs.vc} VarCorr(fit.cs) ``` ## Anova tables The models `ar1`, `toep`, and `us` are nested so we can use: ```{r anova1} anova(fit.ar1, fit.toep, fit.us) ``` `ar1` has the lowest AIC (it's the simplest model, and fits the data adequately); we can't reject the (true in this case!) null model that an AR1 structure is adequate to describe the data. The model `cs` is a sub-model of `toep`: ```{r anova2} anova(fit.cs, fit.toep) ``` Here we *can* reject the null hypothesis of compound symmetry (i.e., that all the pairwise correlations are the same). ## Adding coordinate information Coordinate information can be added to a variable using the `glmmTMB` function `numFactor`. This is necessary in order to use those covariance structures that require coordinates. For example, if we have the numeric coordinates ```{r sample2} x <- sample(1:2, 10, replace=TRUE) y <- sample(1:2, 10, replace=TRUE) ``` we can generate a factor representing $(x,y)$ coordinates by ```{r numFactor} (pos <- numFactor(x,y)) ``` Numeric coordinates can be recovered from the factor levels: ```{r parseNumLevels} parseNumLevels(levels(pos)) ``` In order to try the remaining structures on our test data we re-interpret the time factor using `numFactor`: ```{r numFactor2} dat1$times <- numFactor(dat1$times) levels(dat1$times) ``` ## Ornstein–Uhlenbeck Having the numeric times encoded in the factor levels we can now try the Ornstein–Uhlenbeck covariance structure. ```{r fit.ou} fit.ou <- glmmTMB(y ~ ou(times + 0 | group), data=dat1) fit.ou$sdr$pdHess ## Converged ? ``` It should give the exact same results as `ar1` in this case since the times are equidistant: ```{r fit.ou.vc} VarCorr(fit.ou) ``` However, note the differences between `ou` and `ar1`: - `ou` can handle irregular time points. - `ou` only allows positive correlation between neighboring time points. ## Spatial correlations The structures `exp`, `gau` and `mat` are meant to used for spatial data. They all require a Euclidean distance matrix which is calculated internally based on the coordinates. Here, we will try these models on the simulated time series data. An example with spatial data is presented in a later section. ### Matern ```{r fit.mat} fit.mat <- glmmTMB(y ~ mat(times + 0 | group), data=dat1, dispformula=~0) fit.mat$sdr$pdHess ## Converged ? ``` ```{r fit.mat.vc} VarCorr(fit.mat) ``` ### Gaussian "Gaussian" refers here to a Gaussian decay in correlation with distance, i.e. $\rho = \exp(-d x^2)$, not to the conditional distribution ("family"). ```{r fit.gau} fit.gau <- glmmTMB(y ~ gau(times + 0 | group), data=dat1, dispformula=~0) fit.gau$sdr$pdHess ## Converged ? ``` ```{r fit.gau.vc} VarCorr(fit.gau) ``` ### Exponential ```{r fit.exp} fit.exp <- glmmTMB(y ~ exp(times + 0 | group), data=dat1) fit.exp$sdr$pdHess ## Converged ? ``` ```{r fit.exp.vc} VarCorr(fit.exp) ``` ### A spatial covariance example Starting out with the built in `volcano` dataset we reshape it to a `data.frame` with pixel intensity `z` and pixel position `x` and `y`: ```{r spatial_data} d <- data.frame(z = as.vector(volcano), x = as.vector(row(volcano)), y = as.vector(col(volcano))) ``` Next, add random normal noise to the pixel intensities and extract a small subset of 100 pixels. This is our spatial dataset: ```{r spatial_sub_sample} set.seed(1) d$z <- d$z + rnorm(length(volcano), sd=15) d <- d[sample(nrow(d), 100), ] ``` Display sampled noisy volcano data: ```{r volcano_data_image} volcano.data <- array(NA, dim(volcano)) volcano.data[cbind(d$x, d$y)] <- d$z image(volcano.data, main="Spatial data") ``` Based on this data, we'll attempt to re-construct the original image. As model, it is assumed that the original image `image(volcano)` is a realization of a random field with correlation decaying exponentially with distance between pixels. Denoting by $u(x,y)$ this random field the model for the observations is \[ z_{i} = \mu + u(x_i,y_i) + \varepsilon_i \] To fit the model, a `numFactor` and a dummy grouping variable must be added to the dataset: ```{r spatial_add_pos_and_group} d$pos <- numFactor(d$x, d$y) d$group <- factor(rep(1, nrow(d))) ``` The model is fit by ```{r fit_spatial_model, cache=TRUE} f <- glmmTMB(z ~ 1 + exp(pos + 0 | group), data=d) ``` Recall that a standard deviation `sd=15` was used to distort the image. A confidence interval for this parameter is ```{r confint_sigma} confint(f, "sigma") ``` The glmmTMB `predict` method can predict unseen levels of the random effects. For instance to predict a 3-by-3 corner of the image one could construct the new data: ```{r newdata_corner} newdata <- data.frame( pos=numFactor(expand.grid(x=1:3,y=1:3)) ) newdata$group <- factor(rep(1, nrow(newdata))) newdata ``` and predict using ```{r predict_corner} predict(f, newdata, type="response", allow.new.levels=TRUE) ``` A specific image column can thus be predicted using the function ```{r predict_column} predict_col <- function(i) { newdata <- data.frame( pos = numFactor(expand.grid(1:87,i))) newdata$group <- factor(rep(1,nrow(newdata))) predict(f, newdata=newdata, type="response", allow.new.levels=TRUE) } ``` Prediction of the entire image is carried out by (this takes a while...): ```{r predict_all} pred <- sapply(1:61, predict_col) ``` Finally plot the re-constructed image by ```{r image_results} image(pred, main="Reconstruction") ``` ## Mappings For various advanced purposes, such as computing likelihood profiles, it is useful to know the details of the parameterization of the models - the scale on which the parameters are defined (e.g. standard deviation, variance, or log-standard deviation for variance parameters) and their order. ### Unstructured For an unstructured matrix of size `n`, parameters `1:n` represent the log-standard deviations while the remaining `n(n-1)/2` (i.e. `(n+1):(n:(n*(n+1)/2))`) are the elements of the *scaled* Cholesky factor of the correlation matrix, filled in row-wise order (see [TMB documentation](http://kaskr.github.io/adcomp/classUNSTRUCTURED__CORR__t.html)). In particular, if $L$ is the lower-triangular matrix with 1 on the diagonal and the correlation parameters in the lower triangle, then the correlation matrix is defined as $\Sigma = D^{-1/2} L L^\top D^{-1/2}$, where $D = \textrm{diag}(L L^\top)$. For a single correlation parameter $\theta_0$, this works out to $\rho = \theta_0/(1+\theta_0^2)$. ```{r fit.us.2} vv0 <- VarCorr(fit.us) vv1 <- vv0$cond$group ## extract 'naked' V-C matrix n <- nrow(vv1) rpars <- getME(fit.us,"theta") ## extract V-C parameters ## first n parameters are log-std devs: all.equal(unname(diag(vv1)),exp(rpars[1:n])^2) ## now try correlation parameters: cpars <- rpars[-(1:n)] length(cpars)==n*(n-1)/2 ## the expected number cc <- diag(n) cc[upper.tri(cc)] <- cpars L <- crossprod(cc) D <- diag(1/sqrt(diag(L))) D %*% L %*% D unname(attr(vv1,"correlation")) ``` > FIXME: why are these not quite the same? Not what I expected ```{r other_check} all.equal(c(cov2cor(vv1)),c(fit.us$obj$env$report(fit.us$fit$parfull)$corr[[1]])) ``` Profiling (experimental/exploratory): ```{r fit.us.profile,cache=TRUE} ## want $par, not $parfull: do NOT include conditional modes/'b' parameters ppar <- fit.us$fit$par length(ppar) range(which(names(ppar)=="theta")) ## the last n*(n+1)/2 parameters ## only 1 fixed effect parameter tt <- tmbprofile(fit.us$obj,2,trace=FALSE) ``` ```{r fit.us.profile.plot} plot(tt) confint(tt) ``` ```{r fit.cs.profile,cache=TRUE} ppar <- fit.cs$fit$par length(ppar) range(which(names(ppar)=="theta")) ## the last n*(n+1)/2 parameters ## only 1 fixed effect parameter, 1 dispersion parameter tt2 <- tmbprofile(fit.cs$obj,3,trace=FALSE) ``` ```{r fit.cs.profile.plot} plot(tt2) ``` glmmTMB/inst/doc/troubleshooting.rmd0000644000176200001440000003216213614324717017242 0ustar liggesusers--- title: "Troubleshooting with glmmTMB" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{troubleshooting} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} params: EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true") --- ```{r load_lib,echo=FALSE} library(glmmTMB) knitr::opts_chunk$set(eval = if (isTRUE(exists("params"))) params$EVAL else FALSE) ``` This vignette covers common problems that occur while using `glmmTMB`. The contents will expand with experience. If your problem is not covered below, there's a chance it has been solved in the development version; try updating to the latest version of `glmmTMB` on GitHub. # Warnings ## Model convergence problem; non-positive-definite Hessian matrix; NA values for likelihood/AIC/etc. This warning (`Model convergence problem; non-positive-definite Hessian matrix`) states that at `glmmTMB`'s maximum-likelihood estimate, the curvature of the negative log-likelihood surface is inconsistent with `glmmTMB` really having found the best fit (minimum): instead, the surface is downward-curving, or flat, in some direction(s). It will usually be accompanied by `NA` values for the standard errors, log-likelihood, AIC, and BIC, and deviance. When you run `summary()` on the resulting model, you'll get the warning `In sqrt(diag(vcov)) : NaNs produced`. These problems are most likely: - when a model is overparameterized (i.e. the data does not contain enough information to estimate the parameters reliably) - when a random-effect variance is estimated to be zero, or random-effect terms are estimated to be perfectly correlated ("singular fit": often caused by having too few levels of the random-effect grouping variable) - when zero-inflation is estimated to be near zero (a strongly negative zero-inflation parameter) - when dispersion is estimated to be near zero - when *complete separation* occurs in a binomial model: some categories in the model contain proportions that are either all 0 or all 1 How do we diagnose the problem? ### Example 1. Consider this example: ```{r non-pos-def,cache=TRUE, warning=FALSE} zinbm0 = glmmTMB(count~spp + (1|site), zi=~spp, Salamanders, family=nbinom2) ``` First, see if any of the estimated coefficients are extreme. If you're using a non-identity link function (e.g. log, logit), then parameter values with $|\beta|>10$ are suspect (for a logit link, this implies probabilities very close to 0 or 1; for a log link, this implies mean counts that are close to 0 or extremely large). Inspecting the fixed-effect estimates for this model: ```{r fixef_zinbm0} fixef(zinbm0) ``` The zero-inflation intercept parameter is tiny ($\approx -17$): since the parameters are estimated on the logit scale, we back-transform with `plogis(-17)` to see the at the zero-inflation probability for the baseline level is about $4 \times 10^{-8}$)). Many of the other ZI parameters are very large, compensating for the intercept: the estimated zero-inflation probabilities for all species are ```{r f_zi2} ff <- fixef(zinbm0)$zi round(plogis(c(sppGP=unname(ff[1]),ff[-1]+ff[1])),3) ``` Since the baseline probability is already effectively zero, making the intercept parameter larger or smaller will have very little effect - the likelihood is flat, which leads to the non-positive-definite warning. Now that we suspect the problem is in the zero-inflation component, we can try to come up with ways of simplifying the model: for example, we could use a model that compared the first species ("GP") to the rest: ```{r salfit2,cache=TRUE} Salamanders <- transform(Salamanders, GP=as.numeric(spp=="GP")) zinbm0_A = update(zinbm0, ziformula=~GP) ``` This fits without a warning, although the GP zero-inflation parameter is still extreme: ```{r salfit2_coef,cache=TRUE} fixef(zinbm0_A)[["zi"]] ``` Another possibility would be to fit the variation among species in the zero-inflation parameter as a random effect, rather than a fixed effect: this is slightly more parsimonious. This again fits without an error, although both the average level of zero-inflation and the among-species variation are estimated as very small: ```{r salfit3,cache=TRUE} zinbm0_B = update(zinbm0, ziformula=~(1|spp)) fixef(zinbm0_B)[["zi"]] VarCorr(zinbm0_B) ``` The original analysis considered variation in zero-inflation by site status (mined or not mined) rather than by species - this simpler model only tries to estimate two parameters (mined + difference between mined and no-mining) rather than 7 (one per species) for the zero-inflation model. ```{r zinbm1,cache=TRUE} zinbm1 = glmmTMB(count~spp + (1|site), zi=~mined, Salamanders, family=nbinom2) fixef(zinbm1)[["zi"]] ``` This again fits without a warning, but we see that the zero-inflation is effectively zero in the unmined ("minedno") condition (`plogis(0.38-17.5)` is approximately $4 \times 10^{-8}$). We can estimate the confidence interval, but it takes some extra work: the default Wald standard errors and confidence intervals are useless in this case. ```{r zinbm1_confint,cache=TRUE} ## at present we need to specify the parameter by number; for ## extreme cases need to specify the parameter range ## (not sure why the upper bound needs to be so high ... ?) cc = confint(zinbm1,method="uniroot",parm=9, parm.range=c(-20,20)) print(cc) ``` The lower CI is not defined; the upper CI is -2.08, i.e. we can state that the zero-inflation probability is less than `plogis(-2.08)` = 0.11. More broadly, general inspection of the data (e.g., plotting the response against potential covariates) should help to diagnose overly complex models. ### Example 2. In some cases, scaling predictor variables may help. For example, in this example from @phisanti, the results of `glm` and `glmmTMB` applied to a scaled version of the data set agree, while `glmmTMB` applied to the raw data set gives a non-positive-definite Hessian warning. ```{r fatfiberglmm} ## data taken from gamlss.data:plasma, originally ## http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/plasma.html load(system.file("vignette_data","plasma.rda", package="glmmTMB")) m4.1 <- glm(calories ~ fat*fiber, family = Gamma(link = "log"), data = plasma) m4.2 <- glmmTMB(calories ~ fat*fiber, family = Gamma(link = "log"), data = plasma) ps <- transform(plasma,fat=scale(fat,center=FALSE),fiber=scale(fiber,center=FALSE)) m4.3 <- update(m4.2, data=ps) ## scaling factor for back-transforming standard deviations ss <- c(1, fatsc <- 1/attr(ps$fat,"scaled:scale"), fibsc <- 1/attr(ps$fiber,"scaled:scale"), fatsc*fibsc) ## combine SEs, suppressing the warning from the unscaled model s_vals <- cbind(glm=sqrt(diag(vcov(m4.1))), glmmTMB_unsc=suppressWarnings(sqrt(diag(vcov(m4.2)$cond))), glmmTMB_sc=sqrt(diag(vcov(m4.3)$cond))*ss) print(s_vals,digits=3) ``` ## Example 3. Here is another example (from Samantha Sherman): ```{r load_ss_ex} load(system.file("vignette_data","troubleshooting.rda",package="glmmTMB")) ``` The first model gives the specified warning when it runs, as well as the other symptoms such as `NA` values for the likelihood: ```{r ss_ex_mod1} summary(mod1) ``` We can immediately see that the dispersion is very small and that the zero-inflation parameter is strongly negative. However, we'll develop some fancier machinery that checks the variance-covariance matrix or Hessian of the model, finds eigenvalues that are negative or close to zero, and identifies which model components contribute to those eigenvalues: ```{r diagnose_vcov} diagnose_vcov <- function(model, tol=1e-5, digits=2, analyze_hessian=FALSE) { vv <- vcov(model, full=TRUE) nn <- rownames(vv) if (!all(is.finite(vv))) { if (missing(analyze_hessian)) warning("analyzing Hessian, not vcov") if (!analyze_hessian) stop("can't analyze vcov") analyze_hessian <- TRUE } if (analyze_hessian) { par.fixed <- model$obj$env$last.par.best r <- model$obj$env$random if (!is.null(r)) par.fixed <- par.fixed[-r] vv <- optimHess(par.fixed, fn=model$obj$fn, gr=model$obj$gr) ## note vv is now HESSIAN, not vcov } ee <- eigen(vv) if (all(ee$values>tol)) {message("var-cov matrix OK"); return(invisible(NULL))} ## find negative or small-positive eigenvalues (flat/wrong curvature) bad_evals <- which(ee$values Warning in nlminb(start = par, objective = fn, gradient = gr) : NA/NaN function evaluation This warning occurs when the optimizer visits a region of parameter space that is invalid. It is not a problem as long as the optimizer has left that region of parameter space upon convergence, which is indicated by an absence of the model convergence warnings described above. The following warnings indicate possibly-transient numerical problems with the fit, and can be treated in the same way (i.e. ignored if there are no errors or convergence warnings about the final fitted model). > Cholmod warning 'matrix not positive definite' In older versions of R (< 3.6.0): > Warning in f(par, order = order, ...) : value out of range in 'lgamma' ## false convergence This warning: > false convergence: the gradient ∇f(x) may be computed incorrectly, the other stopping tolerances may be too tight, or either f or ∇f may be discontinuous near the current iterate x comes from the `nlminb` optimizer used by default in `glmmTMB`. It's usually hard to diagnose the source of this warning (this [Stack Overflow answer](https://stackoverflow.com/questions/40039114/r-nlminb-what-does-false-convergence-actually-mean) explains a bit more about what it means). Reasonable methods for making sure your model is OK are: - restart the model at the estimated fitted values - try using a different optimizer, e.g. `control=glmmTMBControl(optimizer=optim, optArgs=list(method="BFGS"))` and see if the results are sufficiently similar to the original fit. # Errors ## NA/NaN gradient evaluation ```{r NA gradient, error=TRUE, warning=FALSE} dat1 = expand.grid(y=-1:1, rep=1:10) m1 = glmmTMB(y~1, dat1, family=nbinom2) ``` The error occurs here because the negative binomial distribution is inappropriate for data with negative values. If you see this error, check that the response variable meets the assumptions of the specified distribution. ## gradient length > Error in nlminb(start = par, objective = fn, gradient = gr) : gradient function must return a numeric vector of length x > Error in optimHess(par.fixed, obj$fn, obj$gr): gradient in optim evaluated to length x Try rescaling predictor variables. Try a simpler model and build up. (If you have a simple reproducible example of these errors, please post them to the issues list.) glmmTMB/inst/doc/model_evaluation.Rnw0000644000176200001440000003740513616054060017324 0ustar liggesusers\documentclass[12pt]{article} %% vignette index specifications need to be *after* \documentclass{} %\VignetteEngine{knitr::knitr} %\VignetteIndexEntry{model evaluation} %\VignettePackage{glmmTMB} %\VignetteDepends{ggplot2} %\VignetteDepends{car} %\VignetteDepends{emmeans} %\VignetteDepends{effects} %\VignetteDepends{multcomp} %\VignetteDepends{MuMIn} %\VignetteDepends{DHARMa} %\VignetteDepends{broom} %\VignetteDepends{broom.mixed} %\VignetteDepends{dotwhisker} %\VignetteDepends{texreg} %\VignetteDepends{xtable} \usepackage{lineno} \usepackage[utf8]{inputenc} \usepackage{graphicx} \usepackage[american]{babel} %% for huxtable \usepackage{array} \usepackage{caption} \usepackage{graphicx} \usepackage{siunitx} \usepackage{colortbl} \usepackage{multirow} \usepackage{hhline} \usepackage{calc} \usepackage{tabularx} \usepackage{threeparttable} \usepackage{wrapfig} \newcommand{\R}{{\sf R}} \newcommand{\fixme}[1]{\textbf{\color{red} fixme: #1}} \newcommand{\notimpl}[1]{\emph{\color{magenta} #1}} \usepackage{url} \usepackage{hyperref} \usepackage{fancyvrb} \usepackage{natbib} %% \code{} below is not safe with \section{} etc. \newcommand{\tcode}[1]{{\tt #1}} \VerbatimFootnotes \bibliographystyle{chicago} %% need this for output of citation() below ... \newcommand{\bold}[1]{\textbf{#1}} %% code formatting %% https://tex.stackexchange.com/questions/273843/inline-verbatim-with-line-breaks-colored-font-and-highlighting/280212 % \usepackage{xcolor} %% see knit_hooks$set(...) below \newcommand\code[1]{\mytokenshelp#1 \relax\relax} \def\mytokenshelp#1 #2\relax{\allowbreak\grayspace\tokenscolor{#1}\ifx\relax#2\else \mytokenshelp#2\relax\fi} %\newcommand\tokenscolor[1]{\colorbox{gray!20}{\textcolor{blue}{% % \ttfamily\mystrut\smash{\detokenize{#1}}}}} \newcommand\tokenscolor[1]{\colorbox{gray!0}{\textcolor{black}{% \ttfamily\mystrut\smash{\detokenize{#1}}}}} \def\mystrut{\rule[\dimexpr-\dp\strutbox+\fboxsep]{0pt}{% \dimexpr\normalbaselineskip-2\fboxsep}} \def\grayspace{\hspace{0pt minus \fboxsep}} \title{Post-model-fitting procedures with \tcode{glmmTMB} models: diagnostics, inference, and model output} \date{\today} \author{} \begin{document} \maketitle %\linenumbers %% TO DO: pipeline for re-running stored objects <>= library("knitr") opts_chunk$set(fig.width=5,fig.height=5, out.width="0.8\\textwidth",echo=TRUE) ## https://tex.stackexchange.com/questions/148188/knitr-xcolor-incompatible-color-definition/254482 knit_hooks$set(document = function(x) {sub('\\usepackage[]{color}', '\\usepackage{xcolor}', x, fixed = TRUE)}) Rver <- paste(R.version$major,R.version$minor,sep=".") used.pkgs <- c("glmmTMB","bbmle") ## packages to report below @ The purpose of this vignette is to describe (and test) the functions in various downstream packages that are available for summarizing and otherwise interpreting glmmTMB fits. Some of the packages/functions discussed below may not be suitable for inference on parameters of the zero-inflation or dispersion models, but will be restricted to the conditional-mean model. <>= library(glmmTMB) library(car) library(emmeans) library(effects) library(multcomp) library(MuMIn) library(DHARMa) library(broom) library(broom.mixed) library(dotwhisker) library(ggplot2); theme_set(theme_bw()) library(texreg) library(xtable) library(huxtable) ## retrieve slow stuff L <- load(system.file("vignette_data","model_evaluation.rda", package="glmmTMB")) @ A couple of example models: % don't need to evaluate this since we have loaded owls_nb1 from model_evaluation.rda <>= owls_nb1 <- glmmTMB(SiblingNegotiation ~ FoodTreatment*SexParent + (1|Nest)+offset(log(BroodSize)), contrasts=list(FoodTreatment="contr.sum", SexParent="contr.sum"), family = nbinom1, zi = ~1, data=Owls) @ <>= data("cbpp",package="lme4") cbpp_b1 <- glmmTMB(incidence/size~period+(1|herd), weights=size,family=binomial, data=cbpp) ## simulated three-term Beta example set.seed(1001) dd <- data.frame(z=rbeta(1000,shape1=2,shape2=3), a=rnorm(1000),b=rnorm(1000),c=rnorm(1000)) simex_b1 <- glmmTMB(z~a*b*c,family=beta_family,data=dd) @ \section{model checking and diagnostics} \subsection{\tcode{DHARMa}} The \code{DHARMa} package provides diagnostics for hierarchical models. After running % set to eval=FALSE since we have this stored in model_evaluation.rda <>= owls_nb1_simres <- simulateResiduals(owls_nb1) @ you can plot the results: <>= plot(owls_nb1_simres) @ \subsubsection{issues} \begin{itemize} \item When you run \code{simulateResiduals()} you'll notice a long warning (actually a \emph{message}: ``It seems you are diagnosing a \code{glmmTMB} model \ldots'' that explains some issues with \code{glmmTMB} fits in \code{DHARMa} \item \code{DHARMa} will only work for models using families for which a simulate method has been implemented (in \code{TMB}, and appropriately reflected in \code{glmmTMB}) \end{itemize} \section{Inference} \subsection{\tcode{car::Anova}} We can use \code{car::Anova()} to get traditional ANOVA-style tables from \code{glmmTMB} fits. A few limitations/reminders: \begin{itemize} \item these tables use Wald $\chi^2$ statistics for comparisons (neither likelihood ratio tests nor $F$ tests) \item they apply to the fixed effects of the conditional component of the model only (other components \emph{might} work, but haven't been tested at all) \item as always, if you want to do type 3 tests, you should probably set sum-to-zero contrasts on factors and center numerical covariates (see contrasts argument above) \end{itemize} <>= if (requireNamespace("car") && getRversion() >= "3.6.0") { Anova(owls_nb1) ## default type II Anova(owls_nb1,type="III") } @ \subsection{effects} <>= effects_ok <- (requireNamespace("effects") && getRversion() >= "3.6.0") if (effects_ok) { (ae <- allEffects(owls_nb1)) plot(ae) } @ (the error can probably be ignored) <>= if (effects_ok) { plot(allEffects(simex_b1)) } @ \subsection{\tcode{emmeans}} <>= emmeans(owls_nb1, poly ~ FoodTreatment | SexParent) @ \subsection{\tcode{drop1}} \code{stats::drop1} is a built-in R function that refits the model with various terms dropped. In its default mode it respects marginality (i.e., it will only drop the top-level interactions, not the main effects): <>= system.time(owls_nb1_d1 <- drop1(owls_nb1,test="Chisq")) @ <>= print(owls_nb1_d1) @ In principle, using \code{scope = . ~ . - (1|Nest)} should work to execute a ``type-3-like'' series of tests, dropping the main effects one at a time while leaving the interaction in (we have to use \code{- (1|Nest)} to exclude the random effects because \code{drop1} can't handle them). However, due to the way that R handles formulas, dropping main effects from an interaction of *factors* has no effect on the overall model. (It would work if we were testing the interaction of continuous variables.) \subsubsection{issues} The \code{mixed} package implements a true ``type-3-like'' parameter-dropping mechanism for \code{[g]lmer} models. Something like that could in principle be applied here. \subsection{Model selection and averaging with \tcode{MuMIn}} We can run \code{MuMIn::dredge(owls_nb1)} on the model to fit all possible submodels. Since this takes a little while (45 seconds or so), we've instead loaded some previously computed results: % stored in vignette_data/model_evaluation.rda ... <>= owls_nb1_dredge @ <>= op <- par(mar=c(2,5,14,3)) plot(owls_nb1_dredge) par(op) ## restore graphics parameters @ Model averaging: <>= model.avg(owls_nb1_dredge) @ \subsubsection{issues} \begin{itemize} \item may not work for Beta models because the \code{family} component ("beta") is not identical to the name of the family function (\code{beta_family()})? (Kamil Bartoń, pers. comm.) \end{itemize} \subsection{\tcode{multcomp} for multiple comparisons and \emph{post hoc} tests} <>= glht_glmmTMB <- function (model, ..., component="cond") { glht(model, ..., coef. = function(x) fixef(x)[[component]], vcov. = function(x) vcov(x)[[component]], df = NULL) } modelparm.glmmTMB <- function (model, coef. = function(x) fixef(x)[[component]], vcov. = function(x) vcov(x)[[component]], df = NULL, component="cond", ...) { multcomp:::modelparm.default(model, coef. = coef., vcov. = vcov., df = df, ...) } @ <>= g1 <- glht(cbpp_b1, linfct = mcp(period = "Tukey")) summary(g1) @ \subsubsection{issues} It is possible to make \code{multcomp} work in a way that (1) actually uses the S3 method structure and (2) doesn't need access to private multcomp methods (i.e. accessed by \code{multcomp:::}) ? Not sure, but both of the following hacks should work. (The \code{glht_glmmTMB} solution below is clunky because it isn't a real S3 method; the \code{model.parm.glmmTMB} solution can't be included in the package source code as-is because ::: is not allowed in CRAN package code.) \section{Extracting coefficients, coefficient plots and tables} \subsection{\tcode{broom} and friends} The \code{broom} and \code{broom.mixed} packages are designed to extract information from a broad range of models in a convenient (tidy) format; the dotwhisker package builds on this platform to draw elegant coefficient plots. <>= if (requireNamespace("broom.mixed") && requireNamespace("dotwhisker")) { (t1 <- broom.mixed::tidy(owls_nb1, conf.int = TRUE)) if (packageVersion("dotwhisker")>"0.4.1") { ## to get this version (which fixes various dotwhisker problems) ## use devtools::install_github("bbolker/broom.mixed") or ## wait for pull request acceptance/submission to CRAN/etc. dwplot(owls_nb1)+geom_vline(xintercept=0,lty=2) } else { owls_nb1$coefficients <- TRUE ## hack! dwplot(owls_nb1,by_2sd=FALSE)+geom_vline(xintercept=0,lty=2) } } @ \subsubsection{issues} (these are more general \code{dwplot} issues) \begin{itemize} \item use black rather than color(1) when there's only a single model, i.e. only add aes(colour=model) conditionally? - draw points even if std err / confint are NA (draw \code{geom_point()} as well as \code{geom_pointrange()}? need to apply all aesthetics, dodging, etc. to both ...) \item for glmmTMB models, allow labeling by component? or should this be done by manipulating the tidied frame first? (i.e.: \code{tidy(.) \%>\% tidyr::unite(term,c(component,term))}) \end{itemize} \subsection{coefficient tables with \tcode{xtable}} The \code{xtable} package can output data frames as \LaTeX\ tables; this isn't quite as elegant as \code{stargazer} etc., but is not a bad start. I've sprinkled lots of hard line-breaks, spaces, and newlines in below: someone who was better at \TeX\ could certainly do a better job. (\code{xtable} can also produce HTML output.) <>= ss <- summary(owls_nb1) ## print table; add space, pxt <- function(x,title) { cat(sprintf("{\n\n\\textbf{%s}\n\\ \\\\\\vspace{2pt}\\ \\\\\n",title)) print(xtable(x), floating=FALSE); cat("\n\n") cat("\\ \\\\\\vspace{5pt}\\ \\\\\n") } <>= pxt(lme4::formatVC(ss$varcor$cond),"random effects variances") pxt(coef(ss)$cond,"conditional fixed effects") pxt(coef(ss)$zi,"conditional zero-inflation effects") @ <>= if (requireNamespace("xtable")) { pxt(lme4::formatVC(ss$varcor$cond),"random effects variances") pxt(coef(ss)$cond,"conditional fixed effects") pxt(coef(ss)$zi,"conditional zero-inflation effects") } @ \subsection{coefficient tables with \tcode{texreg}} <>= source(system.file("other_methods","extract.R",package="glmmTMB")) texreg(owls_nb1,caption="Owls model", label="tab:owls") @ See output in Table~\ref{tab:owls}. \subsection{coefficient tables with \tcode{huxtable}} The \code{huxtable} package allows output in either \LaTeX\ or HTML: this example is tuned for \LaTeX. <>= cc <- c("intercept (mean)"="(Intercept)", "food treatment (starvation)"="FoodTreatment1", "parental sex (M)"="SexParent1", "food $\\times$ sex"="FoodTreatment1:SexParent1") h0 <- huxreg(" "=owls_nb1, # give model blank name so we don't get '(1)' tidy_args=list(effects="fixed"), coefs=cc, error_pos="right", statistics="nobs" # don't include logLik and AIC ) names(h0)[2:3] <- c("estimate","std. err.") ## allow use of math notation in name h1 <- set_cell_properties(h0,row=5,col=1,escape_contents=FALSE) cat(to_latex(h1,tabular_only=TRUE)) @ \subsubsection{issues} \begin{itemize} \item \code{huxtable} needs quite a few additional \LaTeX\ packages: use \code{report_latex_dependencies()} to see what they are. \end{itemize} \section{influence measures} \emph{Influence measures} quantify the effects of particular observations, or groups of observations, on the results of a statistical model; \emph{leverage} and \emph{Cook's distance} are the two most common formats for influence measures. If a \href{https://en.wikipedia.org/wiki/Projection_matrix}{projection matrix} (or ``hat matrix'') is available, influence measures can be computed efficiently; otherwise, the same quantities can be estimated by brute-force methods, refitting the model with each group or observation successively left out. We've adapted the \tcode{car::influence.merMod} function to handle \tcode{glmmTMB} models; because it uses brute force, it can be slow, especially if evaluating the influence of individual observations. For now, it is included as a separate source file rather than exported as a method (see below), although it may be included in the package (or incorporated in the \tcode{car} package) in the future. <>= source(system.file("other_methods","influence_mixed.R", package="glmmTMB")) @ <>= owls_nb1_influence_time <- system.time( owls_nb1_influence <- influence_mixed(owls_nb1, groups="Nest") ) @ Re-fitting the model with each of the \Sexpr{length(unique(Owls$Nest))} nests excluded takes \Sexpr{round(owls_nb1_influence_time[["elapsed"]])} seconds (on an old Macbook Pro). The \tcode{car::infIndexPlot()} function is one way of displaying the results: <>= car::infIndexPlot(owls_nb1_influence) @ Or, you can transform the results and plot them however you like: <>= inf <- as.data.frame(owls_nb1_influence[["fixed.effects[-Nest]"]]) inf <- transform(inf, nest=rownames(inf), cooks=cooks.distance(owls_nb1_influence)) inf$ord <- rank(inf$cooks) if (require(reshape2)) { inf_long <- melt(inf, id.vars=c("ord","nest")) gg_infl <- (ggplot(inf_long,aes(ord,value)) + geom_point() + facet_wrap(~variable, scale="free_y") + scale_x_reverse(expand=expand_scale(mult=0.15)) + scale_y_continuous(expand=expand_scale(mult=0.15)) + geom_text(data=subset(inf_long,ord>24), aes(label=nest),vjust=-1.05) ) print(gg_infl) } @ \section{to do} \begin{itemize} \item more plotting methods (\code{sjplot}) \item output with \code{memisc} \item AUC etc. with \code{ModelMetrics} \end{itemize} <>= ## store time-consuming stuff save("owls_nb1", "owls_nb1_simres", "owls_nb1_dredge", "owls_nb1_influence", "owls_nb1_influence_time", file="../inst/vignette_data/model_evaluation.rda", version=2 ## for compatibility with R < 3.6.0 ) @ \end{document} glmmTMB/inst/doc/glmmTMB.pdf0000644000176200001440000042074313616061763015310 0ustar liggesusers%PDF-1.5 % 32 0 obj << /Length 1142 /Filter /FlateDecode >> stream xڵVMo6W"H}ħ.m#H@5aItI*;CJmj\vp͛hvhvyF߰*㴨*.MxS**7ٮ":36yI9AdmZ[{;8m Zp]tav;kZ~+rɘ(JQq4ș F,D˓Vnm5A.*d9$HHo6 'M=UMWɚSNӥg`ʙrɋqI~7%#YpH7탱?٣Lbh Q*f)H\pAԈ%y!xv=>s;c`{f[H@tT`)A-;BT)|jnQbS=̨m֐5czQ`>/GAPi?=1Hzh|ORW6F!70VarpINhQ C1fwٲZuQ9ljoБP pk:mQˣ!;R&YʢV.ʶ^X[`ٔ%N9 aV@}pz֒w5KM)6@fQHX-K5vOjYwjA;xd+ K5 *){ 5M^:ƐbOAɒw38,_03LyT AޢqK׋E"_>dau z ڼ,е8JJn#?DIz{@"W&5#^7{{l;(N ŚOE=T}vGޏ܌`$f~3yYig#p`e\ѹ>I 䡪j-2!?(:CW} 8|?JL}0餎?j?hVd.*&~n}-"f)iz-(Ndf0ipD:v#튩a~>9<| M.6RA<]ac7!O6ަrQRD *rn8Cn]!_j8{h"Nr^,k_wge% endstream endobj 46 0 obj << /Length 2535 /Filter /FlateDecode >> stream xYoܸ_hIQ-E4g3PjwÑP+Opf8͇n.>\|w{q^ )EfZnR"zH(Yn˥iдa ]qo&IC6qʪ~ 'Eү+~ .ֽz(iG|kC!!- Z].# ˥ e6_"%"bAEepo=vE zK~+6D+m]}qbl#o ?|}@ ]_4`ghy_-:'i8qG8"UkM8ѽHrډB22pvm󒸬o-3nv[I5(ClT_m5h,@65aS'=?ɟ''7丧;I϶4 ڜU`+-JT@Dkvgk39ݓ ,Ͷ9qϙO0k2ILPzFHHrh&oIl-e@ GyeJٍkwmh +8j^S{-bk11Kx!oHОHۓ?ei( UEe"ɦ;؆čg 5!2 KD c/]wsj+~8&becwa""DME鐓54=MU43I6M eUVe""~ͰRZlK.wҀvEc0aDyCt7+ EM~I'!76__X*9I# [4:އ]Bzą$ ܵHੳN5ɀgwIBpGA|;x1B H ܱD. t =Iy[;~৐Sѻ8yZR\4(d=}ZC84C^9YR}0³5R]Qh}U6h8\țw$XfFBSHݧC-{[ŋcxtEr"#S}3ې:7,h4ʆopG~nۓZ 'F'd YM>m_g`>{At(;k[&vvA.aM1{]@qwg'.5 ,pWs 5<):||gif {:ذB6`́}<ΫTqN( X{LiJTyv|W3WDWpQB\| kL(nJ "Aֺ+nQkWt0B=x8q͇G"etY(9S"<fr|I3 lq[J4xݨ[$9Ԝ N-pGZ lܲŪ 1[&4L["VCټ/yqrwQ  )Wl`mV(}Vy%[zꪩy5*Ssg`篨vۋ/(T(oSSr=uuS${Ž[Z-]nV.~ɴ[ÖywN`lb(2m[HC"بyP!fӒvLv}v dL0v@'ji@tvnUi{DwӠα.圹LDYj_@R_5x YqZ9CBflmWy)KO:ϛTG½%=841'=0Phݐ嶦hx9ϗEɦxK|LXU>kUϥ̡^ecp=1":ֳ7`S#S̱LAO9G4 *ͯo8y5㛲ٽ9 kxR#:ʾKY5U|GH>m@01i#':a*J }Fc $iAr_> stream xYo7b~p>S܇HsMw@.a%Q2}(]IpzE)6 HCr7CPDHDND(ϯNξy$%/DEWHRGisg"d [|HVf2cOn'@/;jLc\ٛ:۬zI 7m\gۆ\k%d1 %^6/WϣxPa` D L~ ' 78Ge uVT6tAP%/Lq?"xn@7AD]R\SVЗ6; .Ks%G☐dl&FAxLg9X7: T4X~̇ЅQu߯gg^1#ڵy[:+ޘ3sdP *, [m_Q-UR/yyVRB `?;0]mY=M'TM_Y}yl%G)g{і Jμ3 J|8NP\8 .!@͏Sΰh-R=$seWegt>˰XgpϚC1<. 9qH=Fggv޿{ݗk4399̓yxG&ܣ.ƨb?rv9t鸦Gպj{!G$ۮ6?(.x7LŢOGq3ۇ_i#y_Hŵ1U\k\@x4ǺlL;bN7ϕ߷? rc")0zwC{zgX?_WKJX\t'5=R ֗igj`T‡?uQg)Bنuٛݡ `Q%k7tcd:3G6KEg94󐔕 2dG)4zgʾI]D.WP\9Y2>sGI7=a9ȁa^8WEH! 2.J`,eUuT%{$4;l`rf޺YejXznm}l$LͲb3GM[m 5C9t^P+;iilYWU(6Ǖ0 l Ttmk}f( h|v/:^fS(L`ԁD`vԦC^gB) Sڊ#PdvԎxJj:Qx#T k$-+ UܭA+47czos> cB1 '<3芯~x&9`1jV5Ʈj\#>I1ʂtg/5q,< L :d[yD(G-|!nd}dv$ /U.T0n>wMc2{O,? x^w endstream endobj 69 0 obj << /Length 1585 /Filter /FlateDecode >> stream xkoF}T7!)>4]3tز50`09TޤaАo2.v+r.#`0F!lL9YXռ_,7VyO){V^TUOq?r5 ͲpU".ቲ8@Ee׬kztsȬ&vmn2$Ɨt6r?Έ/yC2M 8;σ]yEkS, ,G%?%wz,G;\ڗA敍2a j5o2`$!8*)0" TȇEBn/ kG?ZZ3Xp+,H4+5LTEò1*;tRı;O *8U Bl*6MC>,UEi歁e|\kirZ dH䪂`f9gt1Fl(z s ǘoQ-+!5VOFdV`(2Bi,ES=dH&*6h;cm9-`k 9 g-X,HYk. ] CXϸ[in,ǛC_SܦQ>+ }(z-@m?Z7ë )L?@Pv=ݧ8)`|M~x=zNMKhm٬kk-bL,]Ypk[NQ^~dp=NN:woݩ>⋶G>QnCA#IBH Qb,wwG)]"O_}Y.Œ~؁pO |Gf&/ +-xdxdQLf][uoضYfԽң>T1\~SC}Ig6r> stream xXoܸ_ȩS"ӦE&pi-Ц.N+$ J!933jŧj>oO V})2'?ЈJ̬E)W .(bcUWۓ=Y7"_ 1ru!gU(Vտ;c*4pvFOM)DҗM]TD5_.y6+v,Bdߋ,굧߲: Kvozd΄t:1]zQKf g;'Uŋ&E'GZrb/wEI2ڌξnۦ%W>n{ߢ~_5-r nɌrΜ,,9ELƧ@ g1o.>=*^(-L:!(hT3k5 i, ;.m[ނ(y. 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Troubleshooting with glmmTMB

2020-02-03

This vignette covers common problems that occur while using glmmTMB. The contents will expand with experience.

If your problem is not covered below, there’s a chance it has been solved in the development version; try updating to the latest version of glmmTMB on GitHub.

Errors

NA/NaN gradient evaluation

dat1 = expand.grid(y=-1:1, rep=1:10)
m1 = glmmTMB(y~1, dat1, family=nbinom2)

The error occurs here because the negative binomial distribution is inappropriate for data with negative values.

If you see this error, check that the response variable meets the assumptions of the specified distribution.

gradient length

Error in nlminb(start = par, objective = fn, gradient = gr) : gradient function must return a numeric vector of length x

Error in optimHess(par.fixed, obj\(fn, obj\)gr): gradient in optim evaluated to length x

Try rescaling predictor variables. Try a simpler model and build up. (If you have a simple reproducible example of these errors, please post them to the issues list.)

glmmTMB/inst/doc/miscEx.rmd0000644000176200001440000000161513614324717015242 0ustar liggesusers--- title: "Miscellaneous examples" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{miscellaneous examples} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- ```{r echo=FALSE} library(glmmTMB) ``` ## Beta dispersion model ```{r simbeta1} set.seed(1001) N <- 1000 mean_pars <- c(1,2) disp_pars <- c(1,2) dd <- data.frame(x=rnorm(N)) m <- plogis(mean_pars[1]+mean_pars[2]*dd$x) d <- exp(disp_pars[1]+disp_pars[2]*dd$x) dd$y <- rbeta(N,shape1=m*d,shape2=(1-m)*d) ``` Fit models: ```{r modbeta1} ## location only m1 <- glmmTMB(y~x, family=beta_family(), data=dd) ## add model for dispersion m2 <- update(m1,dispformula=~x) ``` Fixed effects look close to theoretical values: ```{r coefbeta1} fixef(m2) ``` AIC is insanely much better for the model with dispersion varying: ```{r AICbeta1} bbmle::AICtab(m1,m2) ``` glmmTMB/inst/doc/miscEx.html0000644000176200001440000002666213616061643015433 0ustar liggesusers Miscellaneous examples

Miscellaneous examples

2020-02-03

Beta dispersion model

set.seed(1001)
N <- 1000
mean_pars <- c(1,2)
disp_pars <- c(1,2)
dd <- data.frame(x=rnorm(N))
m <- plogis(mean_pars[1]+mean_pars[2]*dd$x)
d <- exp(disp_pars[1]+disp_pars[2]*dd$x)
dd$y <- rbeta(N,shape1=m*d,shape2=(1-m)*d)

Fit models:

## location only
m1 <- glmmTMB(y~x,
              family=beta_family(),
              data=dd)
## add model for dispersion
m2 <- update(m1,dispformula=~x)

Fixed effects look close to theoretical values:

fixef(m2)
## 
## Conditional model:
## (Intercept)            x  
##       1.005        2.013  
## 
## Dispersion model:
## (Intercept)            x  
##       1.064        1.962

AIC is insanely much better for the model with dispersion varying:

bbmle::AICtab(m1,m2)
##    dAIC   df
## m2    0.0 4 
## m1 1491.6 3
glmmTMB/inst/doc/parallel.html0000644000176200001440000004421313616061732015766 0ustar liggesusers Parallel optimization using glmmTMB

Parallel optimization using glmmTMB

Nafis Sadat

2020-02-03

A new, experimental feature of glmmTMB is the ability to parallelize the optimization process. This vignette shows an example and timing of a simple model fit with and without parallelizing across threads.

If your OS supports OpenMP parallelization and R was installed using OpenMP, glmmTMB will automatically pick up the OpenMP flags from R’s Makevars and compile the C++ model with OpenMP support. If the flag is not available, then the model will be compiled with serial optimization only.

Load packages:

library(glmmTMB)
set.seed(1)
nt <- min(parallel::detectCores(),5)

Simulate a dataset with large N:

N <- 3e5
xdata <- rnorm(N, 1, 2)
ydata <- 0.3 + 0.4*xdata + rnorm(N, 0, 0.25)

First, we fit the model serially. We can pass the number of parallelizing process we want using the parallel parameter in glmmTMBcontrol:

system.time(
  model1 <- glmmTMB(formula = ydata ~ 1 + xdata,
                    control = glmmTMBControl(parallel = 1))
  )
##    user  system elapsed 
##   2.170   0.271   2.474

Now, we fit the same model using five threads (or as many as possible - 4 in this case):

system.time(
  model2 <- glmmTMB(formula = ydata ~ 1 + xdata,
                    control = glmmTMBControl(parallel = nt))
  )
##    user  system elapsed 
##   2.113   0.257   2.396

The speed-up is definitely more visible on models with a much larger number of observations, or in models with random effects.

Here’s an example where we have an IID Gaussian random effect. We first simulate the data with 200 groups (our random effect):

xdata <- rnorm(N, 1, 2)
groups <- 200
data_use <- data.frame(obs = 1:N)
data_use <- within(data_use,
{
  
  group_var <- rep(seq(groups), times = nrow(data_use) / groups)
  group_intercept <- rnorm(groups, 0, 0.1)[group_var]
  xdata <- xdata
  ydata <- 0.3 + group_intercept + 0.5*xdata + rnorm(N, 0, 0.25)
})

We fit the random effect model, first with a single thread:

(t_serial <- system.time(
  model3 <- glmmTMB(formula = ydata ~ 1 + xdata + (1 | group_var), data = data_use, control = glmmTMBControl(parallel = 1))
 )
)
##    user  system elapsed 
##  20.387   2.411  25.761

Now we fit the same model, but using 4 threads. The speed-up is more noticeable with this model.

(t_parallel <- system.time(
     update(model3,  control = glmmTMBControl(parallel = nt))
 )
)
##    user  system elapsed 
##  19.395   2.247  22.870

Notes on OpenMP support

From Writing R Extensions:

Apple builds of clang on macOS currently have no OpenMP support, but CRAN binary packages are built with a clang-based toolchain which supports OpenMP. http://www.openmp.org/resources/openmp-compilers-tools gives some idea of what compilers support what versions.

The performance of OpenMP varies substantially between platforms. The Windows implementation has substantial overheads, so is only beneficial if quite substantial tasks are run in parallel. Also, on Windows new threads are started with the default FPU control word, so computations done on OpenMP threads will not make use of extended-precision arithmetic which is the default for the main process. ## System information

This report was built using 4 parallel threads (on a machine with a total of 4 cores)

print(sessionInfo(), locale=FALSE)
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.2
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] png_0.1-7       ggplot2_3.2.0   lattice_0.20-38 reshape2_1.4.3 
## [5] coda_0.19-3     TMB_1.7.16      MASS_7.3-51.4   glmmTMB_1.0.0  
## [9] knitr_1.23     
## 
## loaded via a namespace (and not attached):
##  [1] zoo_1.8-6           tidyselect_0.2.5    xfun_0.8           
##  [4] purrr_0.3.3         splines_3.6.2       colorspace_1.4-1   
##  [7] stats4_3.6.2        htmltools_0.3.6     yaml_2.2.0         
## [10] survival_3.1-8      rlang_0.4.0         nloptr_1.2.1       
## [13] pillar_1.4.2        glue_1.3.1          withr_2.1.2        
## [16] emmeans_1.4.3.01    multcomp_1.4-10     plyr_1.8.4         
## [19] stringr_1.4.0       munsell_0.5.0       gtable_0.3.0       
## [22] mvtnorm_1.0-11      codetools_0.2-16    evaluate_0.14      
## [25] labeling_0.3        parallel_3.6.2      TH.data_1.0-10     
## [28] Rcpp_1.0.3          xtable_1.8-4        scales_1.0.0       
## [31] lme4_1.1-21         digest_0.6.20       stringi_1.4.3      
## [34] dplyr_0.8.3         numDeriv_2016.8-1.1 tools_3.6.2        
## [37] bbmle_1.0.20        sandwich_2.5-1      magrittr_1.5       
## [40] lazyeval_0.2.2      tibble_2.1.3        crayon_1.3.4       
## [43] pkgconfig_2.0.2     Matrix_1.2-18       estimability_1.3   
## [46] assertthat_0.2.1    minqa_1.2.4         rmarkdown_1.13     
## [49] R6_2.4.0            boot_1.3-23         nlme_3.1-142       
## [52] compiler_3.6.2
glmmTMB/inst/doc/miscEx.R0000644000176200001440000000145213616061643014656 0ustar liggesusers## ----echo=FALSE---------------------------------------------------------- library(glmmTMB) ## ----simbeta1------------------------------------------------------------ set.seed(1001) N <- 1000 mean_pars <- c(1,2) disp_pars <- c(1,2) dd <- data.frame(x=rnorm(N)) m <- plogis(mean_pars[1]+mean_pars[2]*dd$x) d <- exp(disp_pars[1]+disp_pars[2]*dd$x) dd$y <- rbeta(N,shape1=m*d,shape2=(1-m)*d) ## ----modbeta1------------------------------------------------------------ ## location only m1 <- glmmTMB(y~x, family=beta_family(), data=dd) ## add model for dispersion m2 <- update(m1,dispformula=~x) ## ----coefbeta1----------------------------------------------------------- fixef(m2) ## ----AICbeta1------------------------------------------------------------ bbmle::AICtab(m1,m2) glmmTMB/inst/doc/glmmTMB.Rnw0000644000176200001440000003502013614324717015272 0ustar liggesusers\documentclass[12pt]{article} %% vignette index specifications need to be *after* \documentclass{} %\VignetteEngine{knitr::knitr} %\VignetteIndexEntry{basic examples of glmmTMB usage} %\VignettePackage{glmmTMB} %\VignetteDepends{ggplot2} %\VignetteDepends{grid} %\VignetteDepends{bbmle} %\VignetteDepends{mlmRev} %\usepackage{lineno} \usepackage[utf8]{inputenc} \usepackage{graphicx} \usepackage[american]{babel} \newcommand{\R}{{\sf R}} \newcommand{\fixme}[1]{\textbf{\color{red} fixme: #1}} \newcommand{\notimpl}[1]{\emph{\color{magenta} #1}} \usepackage{url} \usepackage{hyperref} \usepackage{alltt} \newcommand{\code}[1]{{\tt #1}} \usepackage{fancyvrb} \usepackage{natbib} \VerbatimFootnotes \bibliographystyle{chicago} %% need this for output of citation() below ... \newcommand{\bold}[1]{\textbf{#1}} \title{Getting started with the \code{glmmTMB} package} \author{Ben Bolker} \date{\today} \begin{document} \maketitle %\linenumbers <>= library("knitr") opts_chunk$set(fig.width=5,fig.height=5, out.width="0.8\\textwidth",echo=TRUE) Rver <- paste(R.version$major,R.version$minor,sep=".") used.pkgs <- c("glmmTMB","bbmle") ## packages to report below @ \section{Introduction/quick start} \code{glmmTMB} is an R package built on the \href{https://github.com/kaskr/adcomp}{Template Model Builder} automatic differentiation engine, for fitting generalized linear mixed models and extensions. (Not-yet-implemented features are denoted \notimpl{like this}) \begin{itemize} \item response distributions: Poisson, binomial, negative binomial (NB1 and NB2 parameterizations), Gamma, Beta, Gaussian; truncated Poisson and negative binomial; \notimpl{Student $t$; Tweedie} \item link functions: log, logit, probit, complementary log-log, inverse, identity \item zero-inflation with fixed and random-effects components; hurdle models via truncated Poisson/NB \item single or multiple (nested or crossed) random effects \item offsets \item fixed-effects models for dispersion \item diagonal, compound-symmetric, or unstructured random effects variance-covariance matrices; first-order autoregressive (AR1) variance structures \end{itemize} In order to use \code{glmmTMB} effectively you should already be reasonably familiar with generalized linear mixed models (GLMMs), which in turn requires familiarity with (i) generalized linear models (e.g. the special cases of logistic, binomial, and Poisson regression) and (ii) `modern' mixed models (those working via maximization of the marginal likelihood rather than by manipulating sums of squares). \cite{bolker_generalized_2009} and \cite{bolker_glmm_2014} are reasonable starting points in this area (especially geared to biologists and less-technical readers), as are \cite{zuur_mixed_2009}, \cite{millar_maximum_2011}, and \cite{zuur_beginners_2013}. In order to fit a model in \code{glmmTMB} you need to: \begin{itemize} \item specify a model for the conditional effects, in the standard R (Wilkinson-Rogers) formula notation (see \code{?formula} or Section 11.1 of the \href{http://cran.r-project.org/doc/manuals/R-intro.pdf}{Introduction to R}. Formulae can also include \emph{offsets}. \item specify a model for the random effects, in the notation that is common to the \code{nlme} and \code{lme4} packages. Random effects are specified as \code{x|g}, where \code{x} is an effect and \code{g} is a grouping factor (which must be a factor variable, or a nesting of/interaction among factor variables). For example, the formula would be \code{1|block} for a random-intercept model or \code{time|block} for a model with random variation in slopes through time across groups specified by \code{block}. A model of nested random effects (block within site) would be \code{1|site/block}; a model of crossed random effects (block and year) would be \code{(1|block)+(1|year)}. \item choose the error distribution by specifying the family (\code{family} argument). In general, you can specify the function (\code{binomial}, \code{gaussian}, \code{poisson}, \code{Gamma} from base R, or one of the options listed at \code{family\_glmmTMB} [\code{nbinom2}, \code{beta\_family()}, \code{betabinomial}, \ldots])). \item choose the error distribution by specifying the family (\code{family} argument). For standard GLM families implemented in R, you can use the function name (\code{binomial}, \code{gaussian}, \code{poisson}, \code{Gamma}). Otherwise, you should specify the family argument as a list containing (at least) the (character) elements \code{family} and \code{link}, e.g. \code{family=list(family="nbinom2",link="log")}. \item optionally specify a zero-inflation model (via the \code{ziformula} argument) with fixed and/or random effects \item optionally specify a dispersion model with fixed effects \end{itemize} This document was generated using \Sexpr{R.version$version.string} and package versions: <>= pkgver <- vapply(sort(used.pkgs),function(x) as.character(packageVersion(x)),"") print(pkgver,quote=FALSE) @ The current citation for \code{glmmTMB} is: \begin{quote} %% fixme: would like to deal with smart quotes <>= print(citation("glmmTMB"),style="latex") @ \end{quote} \section{Preliminaries: packages and data} Load required packages: <>= library("glmmTMB") library("bbmle") ## for AICtab library("ggplot2") ## cosmetic theme_set(theme_bw()+ theme(panel.spacing=grid::unit(0,"lines"))) @ The data, taken from \cite{zuur_mixed_2009} and ultimately from \cite{roulinbersier_2007}, quantify the number of negotiations among owlets (owl chicks) in different nests \emph{prior} to the arrival of a provisioning parent as a function of food treatment (deprived or satiated), the sex of the parent, and arrival time. The total number of calls from the nest is recorded, along with the total brood size, which is used as an offset to allow the use of a Poisson response. Since the same nests are measured repeatedly, the nest is used as a random effect. The model can be expressed as a zero-inflated generalized linear mixed model (ZIGLMM). Various small manipulations of the data set: (1) reorder nests by mean negotiations per chick, for plotting purposes; (2) add log brood size variable (for offset); (3) rename response variable and abbreviate one of the input variables. %% FIXME: I get a warning message ("NAs introduced by coercion") here, but only in knitr, %% and not on a clean start ... ? %% some weird package interaction ? <>= Owls <- transform(Owls, Nest=reorder(Nest,NegPerChick), NCalls=SiblingNegotiation, FT=FoodTreatment) @ (If you were really using this data set you should start with \code{summary(Owls)} to explore the data set.) % fig.cap="Basic view of owl data from \\cite{roulinbersier_2007}." <>= G0 <- ggplot(Owls,aes(x=reorder(Nest,NegPerChick), y=NegPerChick))+ labs(x="Nest",y="Negotiations per chick")+coord_flip()+ facet_grid(FoodTreatment~SexParent) G0+stat_sum(aes(size=..n..),alpha=0.5)+ scale_size_continuous(name="# obs", breaks=seq(1,9,by=2))+ theme(axis.title.x=element_text(hjust=0.5,size=12), axis.text.y=element_text(size=7)) @ We should explore the data before we start to build models, e.g. by plotting it in various ways, but this vignette is about \code{glmmTMB}, not about data visualization \ldots Now fit some models: The basic \code{glmmTMB} fit --- a zero-inflated Poisson model with a single zero-inflation parameter applying to all observations (\verb+ziformula~1+). (Excluding zero-inflation is \code{glmmTMB}'s default: to exclude it explicitly, use \verb+ziformula~0+.) <>= gt1 <- system.time(glmmTMB(NCalls~(FT+ArrivalTime)*SexParent+ offset(log(BroodSize))+(1|Nest), ziformula=~1, data=Owls, family=poisson)) @ <>= fit_zipoisson <- glmmTMB(NCalls~(FT+ArrivalTime)*SexParent+ offset(log(BroodSize))+(1|Nest), data=Owls, ziformula=~1, family=poisson) @ <>= summary(fit_zipoisson) @ We can also try a standard zero-inflated negative binomial model; the default is the ``NB2'' parameterization (variance = $\mu(1+\mu/k)$: \cite{hardin_generalized_2007}). To use families (Poisson, binomial, Gaussian) that are defined in \R, you should specify them as in \code{?glm} (as a string referring to the family function, as the family function itself, or as the result of a call to the family function: i.e. \code{family="poisson"}, \code{family=poisson}, \code{family=poisson()}, and \code{family=poisson(link="log")} are all allowed and all equivalent (the log link is the default for the Poisson family). Some of the additional families that are \emph{not} defined in base R (at this point \code{nbinom2} and \code{nbinom1}) can be specified using the same format. Otherwise, for families that are implemented in \code{glmmTMB} but for which \code{glmmTMB} does not provide a function, you should specify the \code{family} argument as a list containing (at least) the (character) elements \code{family} and \code{link}, e.g. \code{family=list(family="nbinom2",link="log")}. (In order to be able to retrieve Pearson (variance-scaled) residuals from a fit, you also need to specify a \code{variance} component; see \code{?family\_glmmTMB}.) <>= fit_zinbinom <- update(fit_zipoisson,family=nbinom2) @ %% FIXME: caching may lead to %% ## Error in ICtab(..., mnames = mnames, type = "AIC"): memory block of size 3.1 Gb %% downstream, in AICtab() ... %% for now I'm removing caching, but we should %% (1) document this as an issue/make a MWE %% (2) fix it %% (3) we could also cache the AICtab chunk as well .. Alternatively, we can use an ``NB1'' fit (variance = $\phi \mu$). <>= fit_zinbinom1 <- update(fit_zipoisson,family=nbinom1) @ \notimpl{we should have a \code{getFamily} function: ideally it would also specify which are really implemented (although that's harder), and specify default links} Relax the assumption that total number of calls is strictly proportional to brood size (i.e. using log(brood size) as an offset): <>= fit_zinbinom1_bs <- update(fit_zinbinom1, . ~ (FT+ArrivalTime)*SexParent+ BroodSize+(1|Nest)) @ Every change we have made so far improves the fit --- changing distributions improves it enormously, while changing the role of brood size makes only a modest (-1 AIC unit) difference: <>= AICtab(fit_zipoisson,fit_zinbinom,fit_zinbinom1,fit_zinbinom1_bs) @ \subsection{Hurdle models} In contrast to zero-inflated models, hurdle models treat zero-count and non-zero outcomes as two completely separate categories, rather than treating the zero-count outcomes as a mixture of structural and sampling zeros. \code{glmmTMB} includes truncated Poisson and negative binomial familes and hence can fit hurdle models. <>= fit_hnbinom1 <- update(fit_zinbinom1_bs, ziformula=~., data=Owls, family=list(family="truncated_nbinom1",link="log")) @ Then we can use \code{AICtab} to compare all the models. <>= AICtab(fit_zipoisson,fit_zinbinom,fit_zinbinom1,fit_zinbinom1_bs,fit_hnbinom1) @ \section{Sample timings} To get a rough idea of \code{glmmTMB}'s speed relative to \code{lme4} (the most commonly used mixed-model package for R), we try a few standard problems, enlarging the data sets by cloning the original data set (making multiple copies and sticking them together). <>= data("Contraception",package="mlmRev") nc <- nrow(Contraception) nl <- length(levels(Contraception$district)) load("contraceptionTimings.rda") meandiff <- mean(with(tmatContraception, time[pkg=="glmer"]/time[pkg=="glmmTMB"])) @ Figure~\ref{fig:contraception} shows the results of replicating the \code{Contraception} data set (\Sexpr{nc} observations, \Sexpr{nl} levels in the random effects grouping level) from 1 to 40 times. \code{glmmADMB} is sufficiently slow ($\approx 1$ minute for a single copy of the data) that we didn't try replicating very much. On average, \code{glmmTMB} is about \Sexpr{round(meandiff,1)} times faster than \code{glmer} for this problem. <>= ggplot(tmatContraception,aes(n,time,colour=pkg))+geom_point()+ scale_y_log10(breaks=c(1,2,5,10,20,50,100))+ scale_x_log10(breaks=c(1,2,4,10,20,40))+ labs(x="Replication (x 1934 obs.)",y="Elapsed time (s)")+ geom_smooth(method="lm")+ scale_colour_brewer(palette="Set1") @ <>= load("InstEvalTimings.rda") n_InstEval <- 73421L ## seems silly to require lme4 just to get this number meandiff_inst2 <- with(tmatInstEval, time[pkg=="lmer"]/time[pkg=="glmmTMB"]) ggplot(tmatInstEval,aes(n,time,colour=pkg))+geom_point()+ scale_y_log10(breaks=c(1,2,5,10,20,50,100,200))+ scale_x_log10(breaks=c(0.1,0.2,0.5,1.0))+ labs(x=sprintf("Replication (x %d obs.)",n_InstEval), y="Elapsed time (s)")+ geom_smooth(method="lm")+ scale_colour_brewer(palette="Set1") @ Figure~\ref{fig:insteval} shows equivalent timings for the \code{InstEval} data set, although in this case since the original data set is large (\Sexpr{n_InstEval} observations) we subsample the data set rather than cloning it: in this case, the advantage is reversed and \code{lmer} is about \Sexpr{round(1/mean(meandiff_inst2,1))} times faster. In general, we expect \code{glmmTMB}'s advantages over \code{lme4} to be (1) greater flexibility (zero-inflation etc.); (2) greater speed for GLMMs, especially those with large number of ``top-level'' parameters (fixed effects plus random-effects variance-covariance parameters). In contrast, \code{lme4} should be faster for LMMs (for maximum speed, you may want to check the \href{https://github.com/dmbates/MixedModels.jl}{MixedModels.jl} package for Julia); \code{lme4} is more mature and at present has a wider variety of diagnostic checks and methods for using model results, including downstream packages. \bibliography{glmmTMB} \end{document} glmmTMB/inst/doc/sim.rmd0000644000176200001440000000325013614324717014577 0ustar liggesusers--- title: "Simulate from a fitted glmmTMB model" author: "Mollie Brooks" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{simulate} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- `glmmTMB` has the capability to simulate from a fitted model. These simulations resample random effects from their estimated distribution. In future versions of `glmmTMB`, it may be possible to condition on estimated random effects. ```{r setup, include=FALSE, message=FALSE} library(knitr) knitr::opts_chunk$set(echo = TRUE) ``` ```{r libs,message=FALSE} library(glmmTMB) library(ggplot2); theme_set(theme_bw()) ``` Fit a typical model: ```{r fit1} data(Owls) owls_nb1 <- glmmTMB(SiblingNegotiation ~ FoodTreatment*SexParent + (1|Nest)+offset(log(BroodSize)), family = nbinom1, ziformula = ~1, data=Owls) ``` Then we can simulate from the fitted model with the `simulate.glmmTMB` function. It produces a list of simulated observation vectors, each of which is the same size as the original vector of observations. The default is to only simulate one vector (`nsim=1`) but we still return a list for consistency. ```{r sim} simo=simulate(owls_nb1, seed=1) Simdat=Owls Simdat$SiblingNegotiation=simo[[1]] Simdat=transform(Simdat, NegPerChick = SiblingNegotiation/BroodSize, type="simulated") Owls$type = "observed" Dat=rbind(Owls, Simdat) ``` Then we can plot the simulated data against the observed data to check if they are similar. ```{r plots,fig.width=7} ggplot(Dat, aes(NegPerChick, colour=type))+geom_density()+facet_grid(FoodTreatment~SexParent) ``` glmmTMB/inst/doc/sim.R0000644000176200001440000000203213616061734014212 0ustar liggesusers## ----setup, include=FALSE, message=FALSE--------------------------------- library(knitr) knitr::opts_chunk$set(echo = TRUE) ## ----libs,message=FALSE-------------------------------------------------- library(glmmTMB) library(ggplot2); theme_set(theme_bw()) ## ----fit1---------------------------------------------------------------- data(Owls) owls_nb1 <- glmmTMB(SiblingNegotiation ~ FoodTreatment*SexParent + (1|Nest)+offset(log(BroodSize)), family = nbinom1, ziformula = ~1, data=Owls) ## ----sim----------------------------------------------------------------- simo=simulate(owls_nb1, seed=1) Simdat=Owls Simdat$SiblingNegotiation=simo[[1]] Simdat=transform(Simdat, NegPerChick = SiblingNegotiation/BroodSize, type="simulated") Owls$type = "observed" Dat=rbind(Owls, Simdat) ## ----plots,fig.width=7--------------------------------------------------- ggplot(Dat, aes(NegPerChick, colour=type))+geom_density()+facet_grid(FoodTreatment~SexParent) glmmTMB/inst/doc/covstruct.R0000644000176200001440000002147013616061640015461 0ustar liggesusersparams <- list(EVAL = FALSE) ## ----setup, include=FALSE, message=FALSE--------------------------------- library(knitr) library(glmmTMB) library(MASS) ## for mvrnorm() library(TMB) ## for tmbprofile() ## devtools::install_github("kaskr/adcomp/TMB") ## get development version knitr::opts_chunk$set(echo = TRUE, eval=if (isTRUE(exists("params"))) params$EVAL else FALSE) ## turned off caching for now: got error in chunk 'fit.us.2' ## Error in retape() : ## Error when reading the variable: 'thetaf'. Please check data and parameters. ## In addition: Warning message: ## In retape() : Expected object. Got NULL. set.seed(1) ## run this in interactive session if you actually want to evaluate chunks ... ## Sys.setenv(NOT_CRAN="true") ## ----sim1, eval=TRUE----------------------------------------------------- n <- 6 ## Number of time points x <- mvrnorm(mu = rep(0,n), Sigma = .7 ^ as.matrix(dist(1:n)) ) ## Simulate the process using the MASS package y <- x + rnorm(n) ## Add measurement noise ## ----simtimes------------------------------------------------------------ # times <- factor(1:n) # levels(times) ## ----simgroup------------------------------------------------------------ # group <- factor(rep(1,n)) ## ----simcomb------------------------------------------------------------- # dat0 <- data.frame(y,times,group) ## ----fitar1, eval=FALSE-------------------------------------------------- # glmmTMB(y ~ ar1(times + 0 | group), data=dat0) ## ----ar0fit,echo=FALSE--------------------------------------------------- # glmmTMB(y ~ ar1(times + 0 | group), data=dat0) ## ----simGroup------------------------------------------------------------ # simGroup <- function(g, n=6, rho=0.7) { # x <- mvrnorm(mu = rep(0,n), # Sigma = rho ^ as.matrix(dist(1:n)) ) ## Simulate the process # y <- x + rnorm(n) ## Add measurement noise # times <- factor(1:n) # group <- factor(rep(g,n)) # data.frame(y, times, group) # } # simGroup(1) ## ----simGroup2----------------------------------------------------------- # dat1 <- do.call("rbind", lapply(1:1000, simGroup) ) ## ----fit.ar1------------------------------------------------------------- # (fit.ar1 <- glmmTMB(y ~ ar1(times + 0 | group), data=dat1)) ## ----fit.us-------------------------------------------------------------- # fit.us <- glmmTMB(y ~ us(times + 0 | group), data=dat1, dispformula=~0) # fit.us$sdr$pdHess ## Converged ? ## ----fit.us.vc----------------------------------------------------------- # VarCorr(fit.us) ## ----fit.toep------------------------------------------------------------ # fit.toep <- glmmTMB(y ~ toep(times + 0 | group), data=dat1, dispformula=~0) # fit.toep$sdr$pdHess ## Converged ? ## ----fit.toep.vc--------------------------------------------------------- # (vc.toep <- VarCorr(fit.toep)) ## ----fit.toep.vc.diag---------------------------------------------------- # vc1 <- vc.toep$cond[[1]] ## first term of var-cov for RE of conditional model # summary(diag(vc1)) # summary(vc1[row(vc1)!=col(vc1)]) ## ----fit.toep.reml------------------------------------------------------- # fit.toep.reml <- update(fit.toep, REML=TRUE) # vc1R <- VarCorr(fit.toep.reml)$cond[[1]] # summary(diag(vc1R)) # summary(vc1R[row(vc1R)!=col(vc1R)]) ## ----fit.cs-------------------------------------------------------------- # fit.cs <- glmmTMB(y ~ cs(times + 0 | group), data=dat1, dispformula=~0) # fit.cs$sdr$pdHess ## Converged ? ## ----fit.cs.vc----------------------------------------------------------- # VarCorr(fit.cs) ## ----anova1-------------------------------------------------------------- # anova(fit.ar1, fit.toep, fit.us) ## ----anova2-------------------------------------------------------------- # anova(fit.cs, fit.toep) ## ----sample2------------------------------------------------------------- # x <- sample(1:2, 10, replace=TRUE) # y <- sample(1:2, 10, replace=TRUE) ## ----numFactor----------------------------------------------------------- # (pos <- numFactor(x,y)) ## ----parseNumLevels------------------------------------------------------ # parseNumLevels(levels(pos)) ## ----numFactor2---------------------------------------------------------- # dat1$times <- numFactor(dat1$times) # levels(dat1$times) ## ----fit.ou-------------------------------------------------------------- # fit.ou <- glmmTMB(y ~ ou(times + 0 | group), data=dat1) # fit.ou$sdr$pdHess ## Converged ? ## ----fit.ou.vc----------------------------------------------------------- # VarCorr(fit.ou) ## ----fit.mat------------------------------------------------------------- # fit.mat <- glmmTMB(y ~ mat(times + 0 | group), data=dat1, dispformula=~0) # fit.mat$sdr$pdHess ## Converged ? ## ----fit.mat.vc---------------------------------------------------------- # VarCorr(fit.mat) ## ----fit.gau------------------------------------------------------------- # fit.gau <- glmmTMB(y ~ gau(times + 0 | group), data=dat1, dispformula=~0) # fit.gau$sdr$pdHess ## Converged ? ## ----fit.gau.vc---------------------------------------------------------- # VarCorr(fit.gau) ## ----fit.exp------------------------------------------------------------- # fit.exp <- glmmTMB(y ~ exp(times + 0 | group), data=dat1) # fit.exp$sdr$pdHess ## Converged ? ## ----fit.exp.vc---------------------------------------------------------- # VarCorr(fit.exp) ## ----spatial_data-------------------------------------------------------- # d <- data.frame(z = as.vector(volcano), # x = as.vector(row(volcano)), # y = as.vector(col(volcano))) ## ----spatial_sub_sample-------------------------------------------------- # set.seed(1) # d$z <- d$z + rnorm(length(volcano), sd=15) # d <- d[sample(nrow(d), 100), ] ## ----volcano_data_image-------------------------------------------------- # volcano.data <- array(NA, dim(volcano)) # volcano.data[cbind(d$x, d$y)] <- d$z # image(volcano.data, main="Spatial data") ## ----spatial_add_pos_and_group------------------------------------------- # d$pos <- numFactor(d$x, d$y) # d$group <- factor(rep(1, nrow(d))) ## ----fit_spatial_model, cache=TRUE--------------------------------------- # f <- glmmTMB(z ~ 1 + exp(pos + 0 | group), data=d) ## ----confint_sigma------------------------------------------------------- # confint(f, "sigma") ## ----newdata_corner------------------------------------------------------ # newdata <- data.frame( pos=numFactor(expand.grid(x=1:3,y=1:3)) ) # newdata$group <- factor(rep(1, nrow(newdata))) # newdata ## ----predict_corner------------------------------------------------------ # predict(f, newdata, type="response", allow.new.levels=TRUE) ## ----predict_column------------------------------------------------------ # predict_col <- function(i) { # newdata <- data.frame( pos = numFactor(expand.grid(1:87,i))) # newdata$group <- factor(rep(1,nrow(newdata))) # predict(f, newdata=newdata, type="response", allow.new.levels=TRUE) # } ## ----predict_all--------------------------------------------------------- # pred <- sapply(1:61, predict_col) ## ----image_results------------------------------------------------------- # image(pred, main="Reconstruction") ## ----fit.us.2------------------------------------------------------------ # vv0 <- VarCorr(fit.us) # vv1 <- vv0$cond$group ## extract 'naked' V-C matrix # n <- nrow(vv1) # rpars <- getME(fit.us,"theta") ## extract V-C parameters # ## first n parameters are log-std devs: # all.equal(unname(diag(vv1)),exp(rpars[1:n])^2) # ## now try correlation parameters: # cpars <- rpars[-(1:n)] # length(cpars)==n*(n-1)/2 ## the expected number # cc <- diag(n) # cc[upper.tri(cc)] <- cpars # L <- crossprod(cc) # D <- diag(1/sqrt(diag(L))) # D %*% L %*% D # unname(attr(vv1,"correlation")) ## ----other_check--------------------------------------------------------- # all.equal(c(cov2cor(vv1)),c(fit.us$obj$env$report(fit.us$fit$parfull)$corr[[1]])) ## ----fit.us.profile,cache=TRUE------------------------------------------- # ## want $par, not $parfull: do NOT include conditional modes/'b' parameters # ppar <- fit.us$fit$par # length(ppar) # range(which(names(ppar)=="theta")) ## the last n*(n+1)/2 parameters # ## only 1 fixed effect parameter # tt <- tmbprofile(fit.us$obj,2,trace=FALSE) ## ----fit.us.profile.plot------------------------------------------------- # plot(tt) # confint(tt) ## ----fit.cs.profile,cache=TRUE------------------------------------------- # ppar <- fit.cs$fit$par # length(ppar) # range(which(names(ppar)=="theta")) ## the last n*(n+1)/2 parameters # ## only 1 fixed effect parameter, 1 dispersion parameter # tt2 <- tmbprofile(fit.cs$obj,3,trace=FALSE) ## ----fit.cs.profile.plot------------------------------------------------- # plot(tt2) glmmTMB/inst/doc/add_link.md0000644000176200001440000000103113614324717015365 0ustar liggesusers--- title: "adding link functions to glmmTMB" --- - in `glmmTMB.cpp` - add your link to `valid_link` - add your link to the `switch` statement in `inverse_linkfun` (don't forget to `break;`) - run `make enum-update` (this will rebuild `R/enum.R`) - add link to the roxygen documentation in `R/family.R`; rebuild documentation (`make doc-update`) - if your link will be used with binomial responses, add it to `logit_inverse_linkfun` - if your link will be used with neg binomial responses, add it to `log_inverse_linkfun()` glmmTMB/inst/doc/mcmc.R0000644000176200001440000001017713616061642014350 0ustar liggesusers## ----knitr_setup, include=FALSE, message=FALSE--------------------------- library(knitr) opts_chunk$set(echo = TRUE) rc <- knitr::read_chunk rc(system.file("vignette_data","mcmc.R",package="glmmTMB")) ## ----libs,message=FALSE-------------------------------------------------- library(glmmTMB) library(coda) ## MCMC utilities library(reshape2) ## for melt() ## graphics library(lattice) library(ggplot2); theme_set(theme_bw()) ## ----fit1---------------------------------------------------------------- data("sleepstudy",package="lme4") fm1 <- glmmTMB(Reaction ~ Days + (Days|Subject), sleepstudy) ## ----setup--------------------------------------------------------------- ## FIXME: is there a better way for user to extract full coefs? rawcoef <- with(fm1$obj$env,last.par[-random]) names(rawcoef) <- make.names(names(rawcoef),unique=TRUE) ## log-likelihood function ## (MCMCmetrop1R wants *positive* log-lik) logpost_fun <- function(x) -fm1$obj$fn(x) ## check definitions stopifnot(all.equal(c(logpost_fun(rawcoef)), c(logLik(fm1)), tolerance=1e-7)) V <- vcov(fm1,full=TRUE) ## ----run_MCMC------------------------------------------------------------ ##' @param start starting value ##' @param V variance-covariance matrix of MVN candidate distribution ##' @param iterations total iterations ##' @param nsamp number of samples to store ##' @param burnin number of initial samples to discard ##' @param thin thinning interval ##' @param tune tuning parameters; expand/contract V ##' @param seed random-number seed run_MCMC <- function(start, V, logpost_fun, iterations = 10000, nsamp = 1000, burnin = iterations/2, thin = floor((iterations-burnin)/nsamp), tune = NULL, seed=NULL ) { ## initialize if (!is.null(seed)) set.seed(seed) if (!is.null(tune)) { tunesq <- if (length(tune)==1) tune^2 else outer(tune,tune) V <- V*tunesq } chain <- matrix(NA, nsamp+1, length(start)) chain[1,] <- cur_par <- start postval <- logpost_fun(cur_par) j <- 1 for (i in 1:iterations) { proposal = MASS::mvrnorm(1,mu=cur_par, Sigma=V) newpostval <- logpost_fun(proposal) accept_prob <- exp(newpostval - postval) if (runif(1) < accept_prob) { cur_par <- proposal postval <- newpostval } if ((i>burnin) && (i %% thin == 1)) { chain[j+1,] <- cur_par j <- j + 1 } } chain <- na.omit(chain) colnames(chain) <- names(start) chain <- coda::mcmc(chain) return(chain) } ## ----do_run_MCMC,eval=FALSE---------------------------------------------- # t1 <- system.time(m1 <- run_MCMC(start=rawcoef, # V=V, logpost_fun=logpost_fun, # seed=1001)) ## ----load_MCMC, echo=FALSE----------------------------------------------- L <- load(system.file("vignette_data", "mcmc.rda", package="glmmTMB")) ## ----add_names----------------------------------------------------------- colnames(m1) <- c(names(fixef(fm1)[[1]]), "log(sigma)", c("log(sd_Intercept)","log(sd_Days)","cor")) m1[,"cor"] <- sapply(m1[,"cor"],get_cor) ## ----traceplot,fig.width=7----------------------------------------------- xyplot(m1,layout=c(2,3),asp="fill") ## ----effsize------------------------------------------------------------- print(effectiveSize(m1),digits=3) ## ----violins,echo=FALSE-------------------------------------------------- ggplot(reshape2::melt(as.matrix(m1[,-1])),aes(x=Var2,y=value))+ geom_violin(fill="gray")+coord_flip()+labs(x="") ## ----do_tmbstan,eval=FALSE----------------------------------------------- # ## install.packages("tmbstan") # library(tmbstan) # t2 <- system.time(m2 <- tmbstan(fm1$obj)) ## ----show_traceplot,echo=FALSE,fig.width=8,fig.height=5------------------ library(png) library(grid) img <- readPNG(system.file("vignette_data","tmbstan_traceplot.png",package="glmmTMB")) grid.raster(img) glmmTMB/inst/doc/troubleshooting.R0000644000176200001440000001256713616061734016667 0ustar liggesusersparams <- list(EVAL = FALSE) ## ----load_lib,echo=FALSE------------------------------------------------- library(glmmTMB) knitr::opts_chunk$set(eval = if (isTRUE(exists("params"))) params$EVAL else FALSE) ## ----non-pos-def,cache=TRUE, warning=FALSE------------------------------- # zinbm0 = glmmTMB(count~spp + (1|site), zi=~spp, Salamanders, family=nbinom2) ## ----fixef_zinbm0-------------------------------------------------------- # fixef(zinbm0) ## ----f_zi2--------------------------------------------------------------- # ff <- fixef(zinbm0)$zi # round(plogis(c(sppGP=unname(ff[1]),ff[-1]+ff[1])),3) ## ----salfit2,cache=TRUE-------------------------------------------------- # Salamanders <- transform(Salamanders, GP=as.numeric(spp=="GP")) # zinbm0_A = update(zinbm0, ziformula=~GP) ## ----salfit2_coef,cache=TRUE--------------------------------------------- # fixef(zinbm0_A)[["zi"]] ## ----salfit3,cache=TRUE-------------------------------------------------- # zinbm0_B = update(zinbm0, ziformula=~(1|spp)) # fixef(zinbm0_B)[["zi"]] # VarCorr(zinbm0_B) ## ----zinbm1,cache=TRUE--------------------------------------------------- # zinbm1 = glmmTMB(count~spp + (1|site), zi=~mined, Salamanders, family=nbinom2) # fixef(zinbm1)[["zi"]] ## ----zinbm1_confint,cache=TRUE------------------------------------------- # ## at present we need to specify the parameter by number; for # ## extreme cases need to specify the parameter range # ## (not sure why the upper bound needs to be so high ... ?) # cc = confint(zinbm1,method="uniroot",parm=9, parm.range=c(-20,20)) # print(cc) ## ----fatfiberglmm-------------------------------------------------------- # ## data taken from gamlss.data:plasma, originally # ## http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/plasma.html # load(system.file("vignette_data","plasma.rda", package="glmmTMB")) # m4.1 <- glm(calories ~ fat*fiber, family = Gamma(link = "log"), data = plasma) # m4.2 <- glmmTMB(calories ~ fat*fiber, family = Gamma(link = "log"), data = plasma) # ps <- transform(plasma,fat=scale(fat,center=FALSE),fiber=scale(fiber,center=FALSE)) # m4.3 <- update(m4.2, data=ps) # ## scaling factor for back-transforming standard deviations # ss <- c(1, # fatsc <- 1/attr(ps$fat,"scaled:scale"), # fibsc <- 1/attr(ps$fiber,"scaled:scale"), # fatsc*fibsc) # ## combine SEs, suppressing the warning from the unscaled model # s_vals <- cbind(glm=sqrt(diag(vcov(m4.1))), # glmmTMB_unsc=suppressWarnings(sqrt(diag(vcov(m4.2)$cond))), # glmmTMB_sc=sqrt(diag(vcov(m4.3)$cond))*ss) # print(s_vals,digits=3) ## ----load_ss_ex---------------------------------------------------------- # load(system.file("vignette_data","troubleshooting.rda",package="glmmTMB")) ## ----ss_ex_mod1---------------------------------------------------------- # summary(mod1) ## ----diagnose_vcov------------------------------------------------------- # diagnose_vcov <- function(model, tol=1e-5, digits=2, analyze_hessian=FALSE) { # vv <- vcov(model, full=TRUE) # nn <- rownames(vv) # if (!all(is.finite(vv))) { # if (missing(analyze_hessian)) warning("analyzing Hessian, not vcov") # if (!analyze_hessian) stop("can't analyze vcov") # analyze_hessian <- TRUE # } # if (analyze_hessian) { # par.fixed <- model$obj$env$last.par.best # r <- model$obj$env$random # if (!is.null(r)) par.fixed <- par.fixed[-r] # vv <- optimHess(par.fixed, fn=model$obj$fn, gr=model$obj$gr) # ## 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This is often a reasonable shortcut for computing confidence intervals and p-values that allow for finite-sized samples rather than relying on asymptotic sampling distributions. This vignette shows an example of such an analysis. Some caveats: - when using such a "pseudo-Bayesian" approach, be aware that using a scaled likelihood (implicit, improper priors) can often cause problems, especially when the model is poorly constrained by the data - in particular, models with poorly constrained random effects (singular or nearly singular) are likely to give bad results - as shown below, even models that are well-behaved for frequentist fitting may need stronger priors to give well-behaved MCMC results - as with all MCMC analysis, it is the *user's responsibility to check for proper mixing and convergence of the chains* before drawing conclusions - the first MCMC sampler illustrated below is simple (Metropolis with a multivariate normal candidate distribution). Users who want to do MCMC sampling on a regular basis should consider the [tmbstan package](https://CRAN.R-project.org/package=tmbstan), which does much more efficient hybrid/Hamiltonian Monte Carlo sampling. ```{r knitr_setup, include=FALSE, message=FALSE} library(knitr) opts_chunk$set(echo = TRUE) rc <- knitr::read_chunk rc(system.file("vignette_data","mcmc.R",package="glmmTMB")) ``` Load packages: ```{r libs,message=FALSE} library(glmmTMB) library(coda) ## MCMC utilities library(reshape2) ## for melt() ## graphics library(lattice) library(ggplot2); theme_set(theme_bw()) ``` Fit basic model: ```{r fit1} ``` Set up for MCMC: define scaled log-posterior function (in this case the log-likelihood function); extract coefficients and variance-covariance matrices as starting points. ```{r setup} ``` This is a basic block Metropolis sampler, based on Florian Hartig's code [here](https://theoreticalecology.wordpress.com/2010/09/17/metropolis-hastings-mcmc-in-r/). ```{r run_MCMC} ``` Run the chain: ```{r do_run_MCMC,eval=FALSE} ``` ```{r load_MCMC, echo=FALSE} L <- load(system.file("vignette_data", "mcmc.rda", package="glmmTMB")) ``` (running this chain takes `r round(t1["elapsed"],1)` seconds) Add more informative names and transform correlation parameter (see vignette on covariance structures and parameters): ```{r add_names} colnames(m1) <- c(names(fixef(fm1)[[1]]), "log(sigma)", c("log(sd_Intercept)","log(sd_Days)","cor")) m1[,"cor"] <- sapply(m1[,"cor"],get_cor) ``` ```{r traceplot,fig.width=7} xyplot(m1,layout=c(2,3),asp="fill") ``` The trace plots are poor, especially for the correlation; the effective sample size backs this up, as would any other diagnostics we did. ```{r effsize} print(effectiveSize(m1),digits=3) ``` **In a real analysis we would stop and fix the mixing/convergence problems before proceeding**; for this simple sampler, some of our choices would be (1) simply run the chain for longer; (2) tune the candidate distribution (e.g. by using `tune` to scale some parameters, or perhaps by switching to a multivariate Student t distribution [see the `mvtnorm` package]); (3) add regularizing priors. Ignoring the problems and proceeding, we can compute column-wise quantiles or highest posterior density intervals (`coda::HPDinterval`) to get confidence intervals. Plotting posterior distributions, omitting the intercept because it's on a very different scale. ```{r violins,echo=FALSE} ggplot(reshape2::melt(as.matrix(m1[,-1])),aes(x=Var2,y=value))+ geom_violin(fill="gray")+coord_flip()+labs(x="") ``` ## tmbstan The `tmbstan` package allows direct, simple access to a hybrid/Hamiltonian Monte Carlo algorithm for sampling from a TMB object; the `$obj` component of a `glmmTMB` fit is such an object. (To run this example you'll need to install the `tmbstan` package and its dependencies.) ```{r do_tmbstan,eval=FALSE} ``` (running this command, which creates 4 chains, takes `r round(t2["elapsed"],1)` seconds) However, there are many indications (warning messages; trace plots) that the correlation parameter needs to a more informative prior. (In the plot below, the correlation parameter is shown on its unconstrained scale; the actual correlation would be $\theta_3/\sqrt{1+\theta_3^2}$.) ```{r show_traceplot,echo=FALSE,fig.width=8,fig.height=5} library(png) library(grid) img <- readPNG(system.file("vignette_data","tmbstan_traceplot.png",package="glmmTMB")) grid.raster(img) ``` ## To do - solve mixing for cor parameter - more complex example - e.g. `Owls` glmmTMB/inst/doc/model_evaluation.R0000644000176200001440000001746313616061774016773 0ustar liggesusers## ----setopts,echo=FALSE,message=FALSE------------------------------------ library("knitr") opts_chunk$set(fig.width=5,fig.height=5, out.width="0.8\\textwidth",echo=TRUE) ## https://tex.stackexchange.com/questions/148188/knitr-xcolor-incompatible-color-definition/254482 knit_hooks$set(document = function(x) {sub('\\usepackage[]{color}', '\\usepackage{xcolor}', x, fixed = TRUE)}) Rver <- paste(R.version$major,R.version$minor,sep=".") used.pkgs <- c("glmmTMB","bbmle") ## packages to report below ## ----packages,message=FALSE---------------------------------------------- library(glmmTMB) library(car) library(emmeans) library(effects) library(multcomp) library(MuMIn) library(DHARMa) library(broom) library(broom.mixed) library(dotwhisker) library(ggplot2); theme_set(theme_bw()) library(texreg) library(xtable) library(huxtable) ## retrieve slow stuff L <- load(system.file("vignette_data","model_evaluation.rda", package="glmmTMB")) ## ----examples,eval=FALSE------------------------------------------------- # owls_nb1 <- glmmTMB(SiblingNegotiation ~ FoodTreatment*SexParent + # (1|Nest)+offset(log(BroodSize)), # contrasts=list(FoodTreatment="contr.sum", # SexParent="contr.sum"), # family = nbinom1, # zi = ~1, data=Owls) ## ----fit_model3,cache=TRUE----------------------------------------------- data("cbpp",package="lme4") cbpp_b1 <- glmmTMB(incidence/size~period+(1|herd), weights=size,family=binomial, data=cbpp) ## simulated three-term Beta example set.seed(1001) dd <- data.frame(z=rbeta(1000,shape1=2,shape2=3), a=rnorm(1000),b=rnorm(1000),c=rnorm(1000)) simex_b1 <- glmmTMB(z~a*b*c,family=beta_family,data=dd) ## ----dharma_sim,eval=FALSE,message=FALSE--------------------------------- # owls_nb1_simres <- simulateResiduals(owls_nb1) ## ----dharma_plotfig,fig.width=8,fig.height=4----------------------------- plot(owls_nb1_simres) ## ----caranova1----------------------------------------------------------- if (requireNamespace("car") && getRversion() >= "3.6.0") { Anova(owls_nb1) ## default type II Anova(owls_nb1,type="III") } ## ----effects1,fig.width=8,fig.height=4----------------------------------- effects_ok <- (requireNamespace("effects") && getRversion() >= "3.6.0") if (effects_ok) { (ae <- allEffects(owls_nb1)) plot(ae) } ## ----effects2, fig.width=12,fig.height=12-------------------------------- if (effects_ok) { plot(allEffects(simex_b1)) } ## ----emmeans1------------------------------------------------------------ emmeans(owls_nb1, poly ~ FoodTreatment | SexParent) ## ----drop1_eval,cache=TRUE----------------------------------------------- system.time(owls_nb1_d1 <- drop1(owls_nb1,test="Chisq")) ## ----print_drop1--------------------------------------------------------- print(owls_nb1_d1) ## ----dredge1------------------------------------------------------------- owls_nb1_dredge ## ----plot_dredge1,fig.width=8,fig.height=8------------------------------- op <- par(mar=c(2,5,14,3)) plot(owls_nb1_dredge) par(op) ## restore graphics parameters ## ----mumin_MA------------------------------------------------------------ model.avg(owls_nb1_dredge) ## ----glht_def------------------------------------------------------------ glht_glmmTMB <- function (model, ..., component="cond") { glht(model, ..., coef. = function(x) fixef(x)[[component]], vcov. = function(x) vcov(x)[[component]], df = NULL) } modelparm.glmmTMB <- function (model, coef. = function(x) fixef(x)[[component]], vcov. = function(x) vcov(x)[[component]], df = NULL, component="cond", ...) { multcomp:::modelparm.default(model, coef. = coef., vcov. = vcov., df = df, ...) } ## ----glht_use------------------------------------------------------------ g1 <- glht(cbpp_b1, linfct = mcp(period = "Tukey")) summary(g1) ## ----broom_mixed,fig.height=3,fig.width=5-------------------------------- if (requireNamespace("broom.mixed") && requireNamespace("dotwhisker")) { (t1 <- broom.mixed::tidy(owls_nb1, conf.int = TRUE)) if (packageVersion("dotwhisker")>"0.4.1") { ## to get this version (which fixes various dotwhisker problems) ## use devtools::install_github("bbolker/broom.mixed") or ## wait for pull request acceptance/submission to CRAN/etc. dwplot(owls_nb1)+geom_vline(xintercept=0,lty=2) } else { owls_nb1$coefficients <- TRUE ## hack! dwplot(owls_nb1,by_2sd=FALSE)+geom_vline(xintercept=0,lty=2) } } ## ----xtable_prep--------------------------------------------------------- ss <- summary(owls_nb1) ## print table; add space, pxt <- function(x,title) { cat(sprintf("{\n\n\\textbf{%s}\n\\ \\\\\\vspace{2pt}\\ \\\\\n",title)) print(xtable(x), floating=FALSE); cat("\n\n") cat("\\ \\\\\\vspace{5pt}\\ \\\\\n") } ## ----xtable_sum,eval=FALSE----------------------------------------------- # pxt(lme4::formatVC(ss$varcor$cond),"random effects variances") # pxt(coef(ss)$cond,"conditional fixed effects") # pxt(coef(ss)$zi,"conditional zero-inflation effects") ## ----xtable_sum_real,results="asis",echo=FALSE--------------------------- if (requireNamespace("xtable")) { pxt(lme4::formatVC(ss$varcor$cond),"random effects variances") pxt(coef(ss)$cond,"conditional fixed effects") pxt(coef(ss)$zi,"conditional zero-inflation effects") } ## ----texreg1,results="asis"---------------------------------------------- source(system.file("other_methods","extract.R",package="glmmTMB")) texreg(owls_nb1,caption="Owls model", label="tab:owls") ## ----huxtable,results="asis"--------------------------------------------- cc <- c("intercept (mean)"="(Intercept)", "food treatment (starvation)"="FoodTreatment1", "parental sex (M)"="SexParent1", "food $\\times$ sex"="FoodTreatment1:SexParent1") h0 <- huxreg(" "=owls_nb1, # give model blank name so we don't get '(1)' tidy_args=list(effects="fixed"), coefs=cc, error_pos="right", statistics="nobs" # don't include logLik and AIC ) names(h0)[2:3] <- c("estimate","std. err.") ## allow use of math notation in name h1 <- set_cell_properties(h0,row=5,col=1,escape_contents=FALSE) cat(to_latex(h1,tabular_only=TRUE)) ## ----load_infl----------------------------------------------------------- source(system.file("other_methods","influence_mixed.R", package="glmmTMB")) ## ----infl, eval=FALSE---------------------------------------------------- # owls_nb1_influence_time <- system.time( # owls_nb1_influence <- influence_mixed(owls_nb1, groups="Nest") # ) ## ----plot_infl----------------------------------------------------------- car::infIndexPlot(owls_nb1_influence) ## ----plot_infl2,fig.width=8,fig.height=6--------------------------------- inf <- as.data.frame(owls_nb1_influence[["fixed.effects[-Nest]"]]) inf <- transform(inf, nest=rownames(inf), cooks=cooks.distance(owls_nb1_influence)) inf$ord <- rank(inf$cooks) if (require(reshape2)) { inf_long <- melt(inf, id.vars=c("ord","nest")) gg_infl <- (ggplot(inf_long,aes(ord,value)) + geom_point() + facet_wrap(~variable, scale="free_y") + scale_x_reverse(expand=expand_scale(mult=0.15)) + scale_y_continuous(expand=expand_scale(mult=0.15)) + geom_text(data=subset(inf_long,ord>24), aes(label=nest),vjust=-1.05) ) print(gg_infl) } ## ----save_out,echo=FALSE------------------------------------------------- ## store time-consuming stuff save("owls_nb1", "owls_nb1_simres", "owls_nb1_dredge", "owls_nb1_influence", "owls_nb1_influence_time", file="../inst/vignette_data/model_evaluation.rda", version=2 ## for compatibility with R < 3.6.0 ) glmmTMB/inst/doc/mcmc.html0000644000176200001440000107157713616061642015127 0ustar liggesusers post-hoc MCMC with glmmTMB

post-hoc MCMC with glmmTMB

Ben Bolker

2020-02-03

One commonly requested feature is to be able to run a post hoc Markov chain Monte Carlo analysis based on the results of a frequentist fit. This is often a reasonable shortcut for computing confidence intervals and p-values that allow for finite-sized samples rather than relying on asymptotic sampling distributions. This vignette shows an example of such an analysis. Some caveats:

  • when using such a “pseudo-Bayesian” approach, be aware that using a scaled likelihood (implicit, improper priors) can often cause problems, especially when the model is poorly constrained by the data
  • in particular, models with poorly constrained random effects (singular or nearly singular) are likely to give bad results
  • as shown below, even models that are well-behaved for frequentist fitting may need stronger priors to give well-behaved MCMC results
  • as with all MCMC analysis, it is the user’s responsibility to check for proper mixing and convergence of the chains before drawing conclusions
  • the first MCMC sampler illustrated below is simple (Metropolis with a multivariate normal candidate distribution). Users who want to do MCMC sampling on a regular basis should consider the tmbstan package, which does much more efficient hybrid/Hamiltonian Monte Carlo sampling.

Load packages:

library(glmmTMB)
library(coda)     ## MCMC utilities
library(reshape2) ## for melt()
## graphics
library(lattice)
library(ggplot2); theme_set(theme_bw())

Fit basic model:

data("sleepstudy",package="lme4")
fm1 <- glmmTMB(Reaction ~ Days + (Days|Subject),
               sleepstudy)

Set up for MCMC: define scaled log-posterior function (in this case the log-likelihood function); extract coefficients and variance-covariance matrices as starting points.

## FIXME: is there a better way for user to extract full coefs?
rawcoef <- with(fm1$obj$env,last.par[-random])
names(rawcoef) <- make.names(names(rawcoef),unique=TRUE)
## log-likelihood function 
## (MCMCmetrop1R wants *positive* log-lik)
logpost_fun <- function(x) -fm1$obj$fn(x)
## check definitions
stopifnot(all.equal(c(logpost_fun(rawcoef)),
                    c(logLik(fm1)),
          tolerance=1e-7))
V <- vcov(fm1,full=TRUE)

This is a basic block Metropolis sampler, based on Florian Hartig’s code here.

##' @param start starting value
##' @param V variance-covariance matrix of MVN candidate distribution
##' @param iterations total iterations
##' @param nsamp number of samples to store
##' @param burnin number of initial samples to discard
##' @param thin thinning interval
##' @param tune tuning parameters; expand/contract V
##' @param seed random-number seed
run_MCMC <- function(start,
                     V,   
                     logpost_fun,
                     iterations = 10000,
                     nsamp = 1000,
                     burnin = iterations/2,
                     thin = floor((iterations-burnin)/nsamp),
                     tune = NULL,
                     seed=NULL
                     ) {
    ## initialize
    if (!is.null(seed)) set.seed(seed)
    if (!is.null(tune)) {
        tunesq <- if (length(tune)==1) tune^2 else outer(tune,tune)
        V <-  V*tunesq
    }
    chain <- matrix(NA, nsamp+1, length(start))
    chain[1,] <- cur_par <- start
    postval <- logpost_fun(cur_par)
    j <- 1
    for (i in 1:iterations) {
        proposal = MASS::mvrnorm(1,mu=cur_par, Sigma=V)
        newpostval <- logpost_fun(proposal)
        accept_prob <- exp(newpostval - postval)
        if (runif(1) < accept_prob) {
            cur_par <- proposal
            postval <- newpostval
        }
        if ((i>burnin) && (i %% thin == 1)) {
            chain[j+1,] <- cur_par
            j <- j + 1
        }
    }
    chain <- na.omit(chain)
    colnames(chain) <- names(start)
    chain <- coda::mcmc(chain)
    return(chain)
}

Run the chain:

t1 <- system.time(m1 <- run_MCMC(start=rawcoef,
                                 V=V, logpost_fun=logpost_fun,
                                 seed=1001))

(running this chain takes 13.2 seconds)

Add more informative names and transform correlation parameter (see vignette on covariance structures and parameters):

colnames(m1) <- c(names(fixef(fm1)[[1]]),
                  "log(sigma)",
                  c("log(sd_Intercept)","log(sd_Days)","cor"))
m1[,"cor"] <- sapply(m1[,"cor"],get_cor)
xyplot(m1,layout=c(2,3),asp="fill")

The trace plots are poor, especially for the correlation; the effective sample size backs this up, as would any other diagnostics we did.

print(effectiveSize(m1),digits=3)
##       (Intercept)              Days        log(sigma) log(sd_Intercept) 
##               142               227               208               152 
##      log(sd_Days)               cor 
##               170               107

In a real analysis we would stop and fix the mixing/convergence problems before proceeding; for this simple sampler, some of our choices would be (1) simply run the chain for longer; (2) tune the candidate distribution (e.g. by using tune to scale some parameters, or perhaps by switching to a multivariate Student t distribution [see the mvtnorm package]); (3) add regularizing priors.

Ignoring the problems and proceeding, we can compute column-wise quantiles or highest posterior density intervals (coda::HPDinterval) to get confidence intervals. Plotting posterior distributions, omitting the intercept because it’s on a very different scale.

tmbstan

The tmbstan package allows direct, simple access to a hybrid/Hamiltonian Monte Carlo algorithm for sampling from a TMB object; the $obj component of a glmmTMB fit is such an object. (To run this example you’ll need to install the tmbstan package and its dependencies.)

## install.packages("tmbstan")
library(tmbstan)
t2 <- system.time(m2 <- tmbstan(fm1$obj))

(running this command, which creates 4 chains, takes 125.7 seconds)

However, there are many indications (warning messages; trace plots) that the correlation parameter needs to a more informative prior. (In the plot below, the correlation parameter is shown on its unconstrained scale; the actual correlation would be \(\theta_3/\sqrt{1+\theta_3^2}\).)

To do

  • solve mixing for cor parameter
  • more complex example - e.g. Owls
glmmTMB/inst/doc/covstruct.html0000644000176200001440000012506113616061640016225 0ustar liggesusers Covariance structures with glmmTMB

Covariance structures with glmmTMB

Kasper Kristensen

2020-02-03

This vignette demonstrates some of the covariance structures available in the glmmTMB package. Currently the available covariance structures are:

Covariance Notation Parameter count Requirement
Heterogeneous unstructured us \(n(n+1)/2\)
Heterogeneous Toeplitz toep \(2n-1\)
Heterogeneous compound symmetry cs \(n+1\)
Heterogeneous diagonal diag \(n\)
AR(1) ar1 \(2\)
Ornstein–Uhlenbeck ou \(2\) Coordinates
Spatial exponential exp \(2\) Coordinates
Spatial Gaussian gau \(2\) Coordinates
Spatial Matern mat \(3\) Coordinates

The word ‘heterogeneous’ refers to the marginal variances of the model. Beyond correlation parameters, a heterogeneous structure uses \(n\) additional variance parameters where \(n\) is the dimension.

Some of the structures require temporal or spatial coordinates. We will show examples of this in a later section.

The AR(1) covariance structure

Demonstration on simulated data

First, let’s consider a simple time series model. Assume that our measurements \(Y(t)\) are given at discrete times \(t \in \{1,...,n\}\) by

\[Y(t) = \mu + X(t) + \varepsilon(t)\]

where

  • \(\mu\) is the mean value parameter.
  • \(X(t)\) is a stationary AR(1) process, i.e. has covariance \(cov(X(s), X(t)) = \sigma^2\exp(-\theta |t-s|)\).
  • \(\varepsilon(t)\) is iid. \(N(0,\sigma_0^2)\) measurement error.

A simulation experiment is set up using the parameters

Description Parameter Value
Mean \(\mu\) 0
Process variance \(\sigma^2\) 1
Measurement variance \(\sigma_0^2\) 1
One-step correlation \(e^{-\theta}\) 0.7

The following R-code draws a simulation based on these parameter values. For illustration purposes we consider a very short time series.

n <- 6                                              ## Number of time points
x <- mvrnorm(mu = rep(0,n),
             Sigma = .7 ^ as.matrix(dist(1:n)) )    ## Simulate the process using the MASS package
y <- x + rnorm(n)                                   ## Add measurement noise

In order to fit the model with glmmTMB we must first specify a time variable as a factor. The factor levels correspond to unit spaced time points.

times <- factor(1:n)
levels(times)

We also need a grouping variable. In the current case there is only one time-series so the grouping is:

group <- factor(rep(1,n))

We combine the data into a single data frame (not absolutely required, but good practice):

dat0 <- data.frame(y,times,group)

Now fit the model using

glmmTMB(y ~ ar1(times + 0 | group), data=dat0)

This formula notation follows that of the lme4 package.

  • The left hand side of the bar times + 0 corresponds to a design matrix \(Z\) linking observation vector \(y\) (rows) with a random effects vector \(u\) (columns).
  • The distribution of \(u\) is ar1 (this is the only glmmTMB specific part of the formula).
  • The right hand side of the bar splits the above specification independently among groups. Each group has its own separate \(u\) vector but shares the same parameters for the covariance structure.

After running the model, we find the parameter estimates \(\mu\) (intercept), \(\sigma_0^2\) (dispersion), \(\sigma\) (Std. Dev.) and \(e^{-\theta}\) (First off-diagonal of “Corr”) in the output:

FIXME: Try a longer time series when the print.VarCorr is fixed.

Increasing the sample size

A single time series of 6 time points is not sufficient to identify the parameters. We could either increase the length of the time series or increase the number of groups. We’ll try the latter:

simGroup <- function(g, n=6, rho=0.7) {
    x <- mvrnorm(mu = rep(0,n),
             Sigma = rho ^ as.matrix(dist(1:n)) )   ## Simulate the process
    y <- x + rnorm(n)                               ## Add measurement noise
    times <- factor(1:n)
    group <- factor(rep(g,n))
    data.frame(y, times, group)
}
simGroup(1)

Generate a dataset with 1000 groups:

dat1 <- do.call("rbind", lapply(1:1000, simGroup) )

And fitting the model on this larger dataset gives estimates close to the true values (AR standard deviation=1, residual (measurement) standard deviation=1, autocorrelation=0.7):

(fit.ar1 <- glmmTMB(y ~ ar1(times + 0 | group), data=dat1))

The unstructured covariance

We can try to fit an unstructured covariance to the previous dataset dat. For this case an unstructured covariance has 15 correlation parameters and 6 variance parameters. Adding \(\sigma_0^2 I\) on top would cause a strict overparameterization, as these would be redundant with the diagonal elements in the covariance matrix. Hence, when fitting the model with glmmTMB, we have to disable the \(\varepsilon\) term (the dispersion) by setting dispformula=~0:

fit.us <- glmmTMB(y ~ us(times + 0 | group), data=dat1, dispformula=~0)
fit.us$sdr$pdHess ## Converged ?

The estimated variance and correlation parameters are:

VarCorr(fit.us)

{#1_{{#2}}} The estimated correlation is approximately constant along diagonals (apparent Toeplitz structure) and we note that the first off-diagonal is now ca. half the true value (0.7) because the dispersion is effectively included in the estimated covariance matrix (i.e. \(\rho' = \rho {2}{\sigma^2}{AR}/({2}{\sigma^2}{AR} + {2}{sigma^2}{meas})\)).

The Toeplitz structure

The next natural step would be to reduce the number of parameters by collecting correlation parameters within the same off-diagonal. This amounts to 5 correlation parameters and 6 variance parameters.

FIXME: Explain why dispformula=~1 causes over-parameterization

fit.toep <- glmmTMB(y ~ toep(times + 0 | group), data=dat1, dispformula=~0)
fit.toep$sdr$pdHess ## Converged ?

The estimated variance and correlation parameters are:

(vc.toep <- VarCorr(fit.toep))

The diagonal elements are all approximately equal to the true total variance (\({2}{\sigma^2}{AR} + {2}{sigma^2}{meas}\)=2), and the off-diagonal elements are approximately equal to the expected value of 0.7/2=0.35.

vc1 <- vc.toep$cond[[1]] ## first term of var-cov for RE of conditional model
summary(diag(vc1))
summary(vc1[row(vc1)!=col(vc1)])

We can get a slightly better estimate of the variance by using REML estimation (however, the estimate of the correlations seems to have gotten slightly worse):

fit.toep.reml <- update(fit.toep, REML=TRUE)
vc1R <- VarCorr(fit.toep.reml)$cond[[1]]
summary(diag(vc1R))
summary(vc1R[row(vc1R)!=col(vc1R)])

Compound symmetry

The compound symmetry structure collects all off-diagonal elements of the correlation matrix to one common value.

FIXME: Explain why dispformula=~1 causes over-parameterization

fit.cs <- glmmTMB(y ~ cs(times + 0 | group), data=dat1, dispformula=~0)
fit.cs$sdr$pdHess ## Converged ?

The estimated variance and correlation parameters are:

VarCorr(fit.cs)

Anova tables

The models ar1, toep, and us are nested so we can use:

anova(fit.ar1, fit.toep, fit.us)

ar1 has the lowest AIC (it’s the simplest model, and fits the data adequately); we can’t reject the (true in this case!) null model that an AR1 structure is adequate to describe the data.

The model cs is a sub-model of toep:

anova(fit.cs, fit.toep)

Here we can reject the null hypothesis of compound symmetry (i.e., that all the pairwise correlations are the same).

Adding coordinate information

Coordinate information can be added to a variable using the glmmTMB function numFactor. This is necessary in order to use those covariance structures that require coordinates. For example, if we have the numeric coordinates

x <- sample(1:2, 10, replace=TRUE)
y <- sample(1:2, 10, replace=TRUE)

we can generate a factor representing \((x,y)\) coordinates by

(pos <- numFactor(x,y))

Numeric coordinates can be recovered from the factor levels:

parseNumLevels(levels(pos))

In order to try the remaining structures on our test data we re-interpret the time factor using numFactor:

dat1$times <- numFactor(dat1$times)
levels(dat1$times)

Ornstein–Uhlenbeck

Having the numeric times encoded in the factor levels we can now try the Ornstein–Uhlenbeck covariance structure.

fit.ou <- glmmTMB(y ~ ou(times + 0 | group), data=dat1)
fit.ou$sdr$pdHess ## Converged ?

It should give the exact same results as ar1 in this case since the times are equidistant:

VarCorr(fit.ou)

However, note the differences between ou and ar1:

  • ou can handle irregular time points.
  • ou only allows positive correlation between neighboring time points.

Spatial correlations

The structures exp, gau and mat are meant to used for spatial data. They all require a Euclidean distance matrix which is calculated internally based on the coordinates. Here, we will try these models on the simulated time series data.

An example with spatial data is presented in a later section.

Matern

fit.mat <- glmmTMB(y ~ mat(times + 0 | group), data=dat1, dispformula=~0)
fit.mat$sdr$pdHess ## Converged ?
VarCorr(fit.mat)

Gaussian

“Gaussian” refers here to a Gaussian decay in correlation with distance, i.e. \(\rho = \exp(-d x^2)\), not to the conditional distribution (“family”).

fit.gau <- glmmTMB(y ~ gau(times + 0 | group), data=dat1, dispformula=~0)
fit.gau$sdr$pdHess ## Converged ?
VarCorr(fit.gau)

Exponential

fit.exp <- glmmTMB(y ~ exp(times + 0 | group), data=dat1)
fit.exp$sdr$pdHess ## Converged ?
VarCorr(fit.exp)

A spatial covariance example

Starting out with the built in volcano dataset we reshape it to a data.frame with pixel intensity z and pixel position x and y:

d <- data.frame(z = as.vector(volcano),
                x = as.vector(row(volcano)),
                y = as.vector(col(volcano)))

Next, add random normal noise to the pixel intensities and extract a small subset of 100 pixels. This is our spatial dataset:

set.seed(1)
d$z <- d$z + rnorm(length(volcano), sd=15)
d <- d[sample(nrow(d), 100), ]

Display sampled noisy volcano data:

volcano.data <- array(NA, dim(volcano))
volcano.data[cbind(d$x, d$y)] <- d$z
image(volcano.data, main="Spatial data")

Based on this data, we’ll attempt to re-construct the original image.

As model, it is assumed that the original image image(volcano) is a realization of a random field with correlation decaying exponentially with distance between pixels.

Denoting by \(u(x,y)\) this random field the model for the observations is

\[ z_{i} = \mu + u(x_i,y_i) + \varepsilon_i \]

To fit the model, a numFactor and a dummy grouping variable must be added to the dataset:

d$pos <- numFactor(d$x, d$y)
d$group <- factor(rep(1, nrow(d)))

The model is fit by

f <- glmmTMB(z ~ 1 + exp(pos + 0 | group), data=d)

Recall that a standard deviation sd=15 was used to distort the image. A confidence interval for this parameter is

confint(f, "sigma")

The glmmTMB predict method can predict unseen levels of the random effects. For instance to predict a 3-by-3 corner of the image one could construct the new data:

newdata <- data.frame( pos=numFactor(expand.grid(x=1:3,y=1:3)) )
newdata$group <- factor(rep(1, nrow(newdata)))
newdata

and predict using

predict(f, newdata, type="response", allow.new.levels=TRUE)

A specific image column can thus be predicted using the function

predict_col <- function(i) {
    newdata <- data.frame( pos = numFactor(expand.grid(1:87,i)))
    newdata$group <- factor(rep(1,nrow(newdata)))
    predict(f, newdata=newdata, type="response", allow.new.levels=TRUE)
}

Prediction of the entire image is carried out by (this takes a while…):

pred <- sapply(1:61, predict_col)

Finally plot the re-constructed image by

image(pred, main="Reconstruction")

Mappings

For various advanced purposes, such as computing likelihood profiles, it is useful to know the details of the parameterization of the models - the scale on which the parameters are defined (e.g. standard deviation, variance, or log-standard deviation for variance parameters) and their order.

Unstructured

For an unstructured matrix of size n, parameters 1:n represent the log-standard deviations while the remaining n(n-1)/2 (i.e. (n+1):(n:(n*(n+1)/2))) are the elements of the scaled Cholesky factor of the correlation matrix, filled in row-wise order (see TMB documentation). In particular, if \(L\) is the lower-triangular matrix with 1 on the diagonal and the correlation parameters in the lower triangle, then the correlation matrix is defined as \(\Sigma = D^{-1/2} L L^\top D^{-1/2}\), where \(D = \textrm{diag}(L L^\top)\). For a single correlation parameter \(\theta_0\), this works out to \(\rho = \theta_0/(1+\theta_0^2)\).

vv0 <- VarCorr(fit.us)
vv1 <- vv0$cond$group          ## extract 'naked' V-C matrix
n <- nrow(vv1)
rpars <- getME(fit.us,"theta") ## extract V-C parameters
## first n parameters are log-std devs:
all.equal(unname(diag(vv1)),exp(rpars[1:n])^2)
## now try correlation parameters:
cpars <- rpars[-(1:n)]
length(cpars)==n*(n-1)/2      ## the expected number
cc <- diag(n)
cc[upper.tri(cc)] <- cpars
L <- crossprod(cc)
D <- diag(1/sqrt(diag(L)))
D %*% L %*% D
unname(attr(vv1,"correlation"))

FIXME: why are these not quite the same? Not what I expected

all.equal(c(cov2cor(vv1)),c(fit.us$obj$env$report(fit.us$fit$parfull)$corr[[1]]))

Profiling (experimental/exploratory):

## want $par, not $parfull: do NOT include conditional modes/'b' parameters
ppar <- fit.us$fit$par
length(ppar)
range(which(names(ppar)=="theta")) ## the last n*(n+1)/2 parameters
## only 1 fixed effect parameter
tt <- tmbprofile(fit.us$obj,2,trace=FALSE)
plot(tt)
confint(tt)
ppar <- fit.cs$fit$par
length(ppar)
range(which(names(ppar)=="theta")) ## the last n*(n+1)/2 parameters
## only 1 fixed effect parameter, 1 dispersion parameter
tt2 <- tmbprofile(fit.cs$obj,3,trace=FALSE)
plot(tt2)
glmmTMB/inst/doc/glmmTMB.R0000644000176200001440000001127513616061760014731 0ustar liggesusers## ----setopts,echo=FALSE,message=FALSE------------------------------------ library("knitr") opts_chunk$set(fig.width=5,fig.height=5, out.width="0.8\\textwidth",echo=TRUE) Rver <- paste(R.version$major,R.version$minor,sep=".") used.pkgs <- c("glmmTMB","bbmle") ## packages to report below ## ----pkgversions,echo=FALSE---------------------------------------------- pkgver <- vapply(sort(used.pkgs),function(x) as.character(packageVersion(x)),"") print(pkgver,quote=FALSE) ## ----citation,echo=FALSE,results="asis"---------------------------------- print(citation("glmmTMB"),style="latex") ## ----pkgs,message=FALSE-------------------------------------------------- library("glmmTMB") library("bbmle") ## for AICtab library("ggplot2") ## cosmetic theme_set(theme_bw()+ theme(panel.spacing=grid::unit(0,"lines"))) ## ----owltransform,warning=FALSE------------------------------------------ Owls <- transform(Owls, Nest=reorder(Nest,NegPerChick), NCalls=SiblingNegotiation, FT=FoodTreatment) ## ----owlplot1,echo=FALSE,message=FALSE,warning=FALSE,eval=FALSE---------- # G0 <- ggplot(Owls,aes(x=reorder(Nest,NegPerChick), # y=NegPerChick))+ # labs(x="Nest",y="Negotiations per chick")+coord_flip()+ # facet_grid(FoodTreatment~SexParent) # G0+stat_sum(aes(size=..n..),alpha=0.5)+ # scale_size_continuous(name="# obs", # breaks=seq(1,9,by=2))+ # theme(axis.title.x=element_text(hjust=0.5,size=12), # axis.text.y=element_text(size=7)) ## ----time1,echo=FALSE,cache=TRUE----------------------------------------- gt1 <- system.time(glmmTMB(NCalls~(FT+ArrivalTime)*SexParent+ offset(log(BroodSize))+(1|Nest), ziformula=~1, data=Owls, family=poisson)) ## ----glmmTMBfit---------------------------------------------------------- fit_zipoisson <- glmmTMB(NCalls~(FT+ArrivalTime)*SexParent+ offset(log(BroodSize))+(1|Nest), data=Owls, ziformula=~1, family=poisson) ## ----zipoisssum---------------------------------------------------------- summary(fit_zipoisson) ## ----glmmTMBnbinomfit---------------------------------------------------- fit_zinbinom <- update(fit_zipoisson,family=nbinom2) ## ----glmmTMBnbinom1fit--------------------------------------------------- fit_zinbinom1 <- update(fit_zipoisson,family=nbinom1) ## ----glmmTMBnbinom1vfit-------------------------------------------------- fit_zinbinom1_bs <- update(fit_zinbinom1, . ~ (FT+ArrivalTime)*SexParent+ BroodSize+(1|Nest)) ## ----aictab-------------------------------------------------------------- AICtab(fit_zipoisson,fit_zinbinom,fit_zinbinom1,fit_zinbinom1_bs) ## ----glmmTMBnbinomhfit,cache=TRUE---------------------------------------- fit_hnbinom1 <- update(fit_zinbinom1_bs, ziformula=~., data=Owls, family=list(family="truncated_nbinom1",link="log")) ## ----hurdle_AIC---------------------------------------------------------- AICtab(fit_zipoisson,fit_zinbinom,fit_zinbinom1,fit_zinbinom1_bs,fit_hnbinom1) ## ----contraception_sum,echo=FALSE---------------------------------------- data("Contraception",package="mlmRev") nc <- nrow(Contraception) nl <- length(levels(Contraception$district)) load("contraceptionTimings.rda") meandiff <- mean(with(tmatContraception, time[pkg=="glmer"]/time[pkg=="glmmTMB"])) ## ----contraception,echo=FALSE,warning=FALSE,fig.cap="Timing for fitting the replicated Contraception data set."---- ggplot(tmatContraception,aes(n,time,colour=pkg))+geom_point()+ scale_y_log10(breaks=c(1,2,5,10,20,50,100))+ scale_x_log10(breaks=c(1,2,4,10,20,40))+ labs(x="Replication (x 1934 obs.)",y="Elapsed time (s)")+ geom_smooth(method="lm")+ scale_colour_brewer(palette="Set1") ## ----insteval,echo=FALSE,warning=FALSE,fig.cap="Timing for fitting subsets of the InstEval data set."---- load("InstEvalTimings.rda") n_InstEval <- 73421L ## seems silly to require lme4 just to get this number meandiff_inst2 <- with(tmatInstEval, time[pkg=="lmer"]/time[pkg=="glmmTMB"]) ggplot(tmatInstEval,aes(n,time,colour=pkg))+geom_point()+ scale_y_log10(breaks=c(1,2,5,10,20,50,100,200))+ scale_x_log10(breaks=c(0.1,0.2,0.5,1.0))+ labs(x=sprintf("Replication (x %d obs.)",n_InstEval), y="Elapsed time (s)")+ geom_smooth(method="lm")+ scale_colour_brewer(palette="Set1") glmmTMB/inst/other_methods/0000755000176200001440000000000013614324717015402 5ustar liggesusersglmmTMB/inst/other_methods/effectsglmmTMB.R0000644000176200001440000001342313614324717020367 0ustar liggesusers## modified from car::effectsmer.R ## effect.mer and effect.lme built from effect.lm by S. Weisberg 29 June 2011 ## 2012-03-08 to require() lme4 or nlme. J. Fox ## 2012-10-05 effect.lme didn't work with 'weights', now corrected. S. Weisberg ## 2013-03-05: introduced merMod methods for development version of lme4. J. Fox ## 2013-04-06: added support for lme4.0, J. Fox ## 2013-07-30: added 'data' argument to lme.to.glm and mer.to.glm to allow ## calling effect from within a subroutine. ## 2013-09-25: removed the 'data' argument as it makes the functions fail with ## logs, splines and polynomials ## 2014-09-24: added option for KR cov matrix to mer.to.glm(). J. Fox ## 2014-12-07: don't assume that pbkrtest is installed. J. Fox ## 2014-12-20: mer.to.glm failed for negative.binomial() because the link has an argument ## that was handled incorrectly by the family.glmResp function. This function is no longer ## used by mer.to.glm. The same error will recur in any link with an argument. ## 2015-06-10: requireNamespace("pbkrtest") rather than require("pbkrtest) ## 2015-07-02: fixed bug when the name of the data frame was the name of a function (e.g., sort, or lm) ## 2015-12-13: make it work with pbkrtest 0.4-3. J. Fox ## 2016-01-07: modified 'fixmod' to allow "||" in variance formulae ## 2016-01-19: Fixed bug in glm.to.mer when 'poly' is used in a model. ## 2016-06-08: Fixed bug handling the 'start' argument in glm.to.mer. Fix by Ben Bolker, bug from Mariano Devoto ## 2016-11-18: Change to mer.to.glm and lme.to.glm for stability in unusual glms. By Nate TeGrotenhuis. ## 2017-03-28: in mer.to.glm, changed a name from m to .m. The fake glm is created from the mer model's ## call slot, which included the name of the data.frame, if any. A data.frame named 'm' ## therefore did not work. Same bug fixed in lme.to.glm ## the function lm.wfit gets the hessian wrong for mer's. Get the variance ## from the vcov method applied to the mer object. ## mer.to.glm evaluates a 'glm' model that is as similar to a given 'mer' ## model as possible. It is of class c("fakeglm", "glm", "lm") ## several items are added to the created objects. Do not export #' @importFrom lme4 nobars glmmTMB.to.glm <- function(mod, KR=FALSE) { if (KR) { ## && !requireNamespace("pbkrtest", quietly=TRUE)){ KR <- FALSE warning("pbkrtest is not compatible with glmmTMB, KR set to FALSE") } ## object$family$family doesn't work correctly with the negative binomial family because of the # argument in the family function, so the old line # family <- family(mod) # returns an error message for these models. The following kluge fixes this. # If this bug is fixed in lme4, this code may break because it expects resp$family$family # to return "Link Name(arg)" with ONE argument, and so spaces between Name and "(arg)" family1 <- function(object, ...){ famname <- family(object)$family open.paren <- regexpr("\\(", famname) if(open.paren==-1) { name <- famname arg <- list() } else { name <- sub(" ", ".", tolower(substr(famname, 1, -1 + open.paren))) arg <- list(as.numeric(gsub("\\)", "", substr(famname, 1 + open.paren, 100)))) } if(is.null(family(object)$initialize)) do.call(name, arg) else family(object) } family <- family1(mod) # end link <- family$link family <- family$family cl <- getCall(mod) cl$control <- glm.control(epsilon=1) # suggested by Nate TeGrotenhuis ## if(cl[[1]] =="nlmer") stop("effects package does not support 'nlmer' objects") .m <- match(c("formula", "family", "data", "weights", "subset", "na.action", "offset", "model", "contrasts"), names(cl), 0L) cl <- cl[c(1L, .m)] cl[[1L]] <- as.name("glm") cl$formula <- lme4::nobars(as.formula(cl$formula)) # cl$data <- mod@frame # caused bug with a 'poly' in the formula cl$family <- gaussian mod2 <- eval(cl) cl$family <- family mod2$coefficients <- glmmTMB::fixef(mod)[["cond"]] ## mod2$vcov <- if (family == "gaussian" && link == "identity" && KR) as.matrix(pbkrtest::vcovAdj(mod)) else as.matrix(vcov(mod)) mod2$vcov <- vcov(mod)[["cond"]] mod2$linear.predictors <- model.matrix(mod2) %*% mod2$coefficients mod2$fitted.values <- mod2$family$linkinv(mod2$linear.predictors) mod2$weights <- as.vector(with(mod2, prior.weights * (family$mu.eta(linear.predictors)^2 / family$variance(fitted.values)))) mod2$residuals <- with(mod2, prior.weights * (y - fitted.values)/weights ) class(mod2) <- c("fakeglm", class(mod2)) mod2 } ##method for 'fakeglm' objects. Do not export vcov.fakeglm <- function(object, ...) object$vcov ##The next six functions should be exported as S3 methods #' @export effect.glmmTMB <- function(term, mod, vcov.=vcov, KR=FALSE, ...) { result <- effect(term, glmmTMB.to.glm(mod, KR=KR), vcov., ...) result$formula <- as.formula(formula(mod)) result } #' @export allEffects.glmmTMB <- function(mod, KR=FALSE,...){ allEffects(glmmTMB.to.glm(mod,KR=KR), ...) } ## testing if (FALSE) { ## working directory: inst/other_methods/ source("effectsglmmTMB.R") library(effects) gg1 <- glmmTMB(round(Reaction)~Days+(1|Subject),family=poisson,data=lme4::sleepstudy) glmmTMB.to.glm(gg1) allEffects(gg1) set.seed(101) dd <- data.frame(y=rnbinom(1000,mu=4,size=1), x = rnorm(1000), f=factor(rep(LETTERS[1:20],each=50))) gg2 <- glmmTMB(y~x+(1|f),family=nbinom2,data=dd) ls(environment(gg2$modelInfo$family$dev.resids),all.names=TRUE) allEffects(gg2) } glmmTMB/inst/other_methods/extract.R0000644000176200001440000000400713614324717017200 0ustar liggesusers ## methods for texreg ##' @param model a glmmTMB model ##' @export ##' @importFrom stats coef ##' @rawNamespace if(requireNamespace("texreg")) importFrom(texreg,extract) ##' extract.glmmTMB <- function(model) { cc <- coef(summary(model)) cc <- cc[!vapply(cc,is.null,logical(1))] nn <- names(cc) pref <- ifelse(nn=="cond","",paste0(nn,"_")) names <- unlist(Map(function(x,y) paste0(x,rownames(y)), pref,cc)) co <- unlist(lapply(cc,function(x) x[,"Estimate"])) se <- unlist(lapply(cc,function(x) x[,"Std. Error"])) pval <- unlist(lapply(cc,function(x) x[,"Pr(>|z|)"])) ## alternately could do something like ## rownames(unlist(cc)) -> gsub("\\.","_",.) ## -> gsub("cond_","") if (!requireNamespace("texreg")) { stop("must have texreg package installed") } tr <- texreg::createTexreg( coef.names = names, coef = co, se = se, pvalues = pval ## use AIC/BIC/logLik? ## gof.names = gof.names, ## gof = gof ) return(tr) } verbatim <- function(x) { txt <- trimws(capture.output(x)) txt <- c("\\begin{verbatim}", txt, "\\end{verbatim}") cat(txt, sep="\n") } ## setMethod("extract", signature = className("glmmTMB", "glmmTMB"), ## definition = extract.glmmTMB) #' @param x an expression to evaluate #' @param stop_ex regular expression for truncating output #' @param stop_which which occurrence of \code{stop_ex} to find #' @importFrom utils capture.output verbatim <- function(x, stop_ex=NULL, stop_which=1) { txt <- trimws(capture.output(x)) if (!is.null(stop_ex)) { txt <- txt[seq(grep(stop_ex,txt)[stop_which]-1)] } txt <- c("\\begin{verbatim}", txt, "\\end{verbatim}") cat(txt, sep="\n") } setOldClass("glmmTMB") setMethod("extract","glmmTMB",extract.glmmTMB) ## m1 <- glmmTMB(formula = count ~ mined + (1 | site), data = Salamanders, ## family = poisson, ziformula = ~mined, dispformula = ~1) ## verbatim(summary(m1), "Random effects:") glmmTMB/inst/other_methods/car_methods.R0000644000176200001440000000164213614324717020020 0ustar liggesusers## EXPERIMENTAL (not working, not yet exported) ## modified from car::Anova.default Anova.glmmTMB <- function (mod, type = c("II", "III", 2, 3), test.statistic = c("Chisq", "F"), vcov. = vcov(mod)[["cond"]], singular.ok, ...) { stop("not finished yet") if (is.function(vcov.)) vcov. <- vcov.(mod) type <- as.character(type) type <- match.arg(type) test.statistic <- match.arg(test.statistic) if (missing(singular.ok)) singular.ok <- type == "2" || type == "II" switch(type, II = Anova.II.default(mod, vcov., test.statistic, singular.ok = singular.ok), III = Anova.III.default(mod, vcov., test.statistic, singular.ok = singular.ok), `2` = Anova.II.default(mod, vcov., test.statistic, singular.ok = singular.ok), `3` = Anova.III.default(mod, vcov., test.statistic, singular.ok = singular.ok)) } glmmTMB/inst/other_methods/influence_mixed.R0000644000176200001440000002100013614324717020654 0ustar liggesusers# added 2017-12-13 by J. Fox # 2017-12-14: improved recovery of model data # removed faulty one-step approximations # 2018-01-28: fix computation of Cook's D for lme models # 2018-05-23: fixed bug when more than one grouping variable (reported by Maarten Jung) # 2018-06-07: skip plot of "sigma^2" in GLMM if dispersion fixed to 1; improved labelling for covariance components # 2018-11-04: tweak to dfbetas.influence.merMod() suggested by Ben Bolker. # 2018-11-09: parallel version of influence.merMod() ## influence diagnostics for mixed models ## copied from car (unexported, don't want to rely on car:::combineLists) combineLists <- function(..., fmatrix="list", flist="c", fvector="rbind", fdf="rbind", recurse=FALSE){ # combine lists of the same structure elementwise # ...: a list of lists, or several lists, each of the same structure # fmatrix: name of function to apply to matrix elements # flist: name of function to apply to list elements # fvector: name of function to apply to data frame elements # recurse: process list element recursively frecurse <- function(...){ combineLists(..., fmatrix=fmatrix, fvector=fvector, fdf=fdf, recurse=TRUE) } if (recurse) flist="frecurse" list.of.lists <- list(...) if (length(list.of.lists) == 1){ list.of.lists <- list.of.lists[[1]] list.of.lists[c("fmatrix", "flist", "fvector", "fdf")] <- c(fmatrix, flist, fvector, fdf) return(do.call("combineLists", list.of.lists)) } if (any(!sapply(list.of.lists, is.list))) stop("arguments are not all lists") len <- sapply(list.of.lists, length) if (any(len[1] != len)) stop("lists are not all of the same length") nms <- lapply(list.of.lists, names) if (any(unlist(lapply(nms, "!=", nms[[1]])))) stop("lists do not all have elements of the same names") nms <- nms[[1]] result <- vector(len[1], mode="list") names(result) <- nms for(element in nms){ element.list <- lapply(list.of.lists, "[[", element) # clss <- sapply(element.list, class) clss <- lapply(element.list, class) # if (any(clss[1] != clss)) stop("list elements named '", element, if (!all(vapply(clss, function(e) all(e == clss[[1L]]), NA))) stop("list elements named '", element, "' are not all of the same class") is.df <- is.data.frame(element.list[[1]]) fn <- if (is.matrix(element.list[[1]])) fmatrix else if (is.list(element.list[[1]]) && !is.df) flist else if (is.vector(element.list[[1]])) fvector else if (is.df) fdf else stop("list elements named '", element, "' are not matrices, lists, vectors, or data frames") result[[element]] <- do.call(fn, element.list) } result } ## copied from lme4 namedList <- function (...) { L <- list(...) snm <- sapply(substitute(list(...)), deparse)[-1] if (is.null(nm <- names(L))) nm <- snm if (any(nonames <- nm == "")) nm[nonames] <- snm[nonames] setNames(L, nm) } ##' compute influence measures for [g]lmerMod (from lme4) or glmmTMB (from glmmTMB) fitted models ##' @param model fitted model ##' @param groups (character) names of group(s) to leave out/compute sensitivity for: ".case" means observation-level sensitivity ##' @param data data frame to use in refitting (will be taken from the model call if possible, but it may be necessary to specify this if the influence measures are being computed in a new environment) ##' @param maxfun maximum number of function evaluations ##' @param ncores number of parallel cores to use for computation ##' @param component for glmmTMB models, which component to use (conditional, zero-inflated, or dispersion) influence_mixed <- function(model, groups=".case", data, maxfun=1000, ncores=getOption("mc.cores", 2L), component=c("cond","zi","disp"), progress=FALSE, ...) { component <- match.arg(component) if (inherits(model,"glmmTMB")) { fe <- function(x) fixef(x)[[component]] VC <- function(x) VarCorr(x)[[component]] .vcov <- function(x) Matrix::as.matrix(vcov(x)[[component]]) } else { fe <- fixef VC <- VarCorr .vcov <- function(x) Matrix::as.matrix(vcov(x)) } if (is.infinite(ncores)) { ncores <- parallel::detectCores(logical=FALSE) } if (missing(data)){ data <- getCall(model)$data data <- if (!is.null(data)) eval(data, parent.frame()) else stop("model did not use the data argument") } if (".case" %in% groups) { data$.case <- rownames(data) } else if (length(groups) > 1){ del.var <- paste0(groups, collapse=".") ## Reduce(interaction,groups) ? data[, del.var] <- apply(data, 1, paste0, collapse=".") groups <- del.var } unique.del <- unique(data[, groups]) data[[".groups"]] <- data[, groups] par <- list(theta=getME(model, "theta")) if (inherits(model, "glmerMod") || inherits(model,"glmmTMB")) { par$beta <- fe(model) } fixed <- fe(model) Vs <- VC(model) nms <- names(Vs) sep <- ":" if (length(nms) == 1) { nms <- "" sep <- "" } vc <- sigma(model)^2 names(vc) <- "sigma^2" for (i in 1:length(Vs)){ V <- Vs[[i]] c.names <- colnames(V) e.names <- outer(c.names, c.names, function(a, b) paste0("C[", a, ",", b, "]")) diag(e.names) <- paste0("v[", c.names, "]") v <- V[lower.tri(V, diag=TRUE)] names(v) <- paste0(nms[i], sep, e.names[lower.tri(e.names, diag=TRUE)]) vc <- c(vc, v) } if (inherits(model,"lmerMod")) { control <- lme4::lmerControl(optCtrl=list(maxfun=maxfun)) } else if (inherits(model, "glmerMod")) { control <- lme4:: glmerControl(optCtrl=list(maxfun=maxfun)) } else if (inherits(model, "glmmTMB")) { ## ??? control <- glmmTMBControl(optCtrl=list(iter.max=maxfun,eval.max=maxfun)) } deleteGroup <- function(del){ if (progress) cat(".") data_sub <- data[data$.groups!=del,] mod.1 <- suppressWarnings(update(model, data=data_sub, start=par, control=control)) ## platform-independent? if (inherits(mod.1,"glmmTMB")) { feval <- mod.1$fit$evaluations[["function"]] converged <- mod.1$fit$convergence==0 } else { ## [g]lmerMod opt <- mod.1@optinfo feval <- opt$feval converged <- opt$conv$opt == 0 && length(opt$warnings) == 0 } fixed.1 <- fe(mod.1) Vs.1 <- VC(mod.1) vc.0 <- sigma(mod.1)^2 for (V in Vs.1){ vc.0 <- c(vc.0, V[lower.tri(V, diag=TRUE)]) } vc.1 <- vc.0 vcov.1 <<- .vcov(mod.1) namedList(fixed.1, vc.1, vcov.1, converged, feval) } result <- if(ncores >= 2){ message("using a cluster of ", ncores, " cores") cl <- parallel::makeCluster(ncores) on.exit(parallel::stopCluster(cl)) parallel::clusterExport(cl, c("namedList", "combineLists")) parallel::clusterEvalQ(cl, require("lme4")) parallel::clusterEvalQ(cl, require("glmmTMB")) parallel::clusterApply(cl, unique.del, deleteGroup) } else { lapply(unique.del, deleteGroup) } result <- combineLists(result) fixed.1 <- result$fixed.1 rownames(fixed.1) <- unique.del colnames(fixed.1) <- names(fixed) vc.1 <- result$vc.1 rownames(vc.1) <- unique.del colnames(vc.1) <- names(vc) feval <- as.vector(result$feval) converged <- as.vector(result$converged) vcov.1 <- result$vcov.1 names(vcov.1) <- names(feval) <- names(converged) <- unique.del left <- "[-" right <- "]" if (groups == ".case") { groups <- "case" } nms <- c("fixed.effects", paste0("fixed.effects", left, groups, right), "var.cov.comps", paste0("var.cov.comps", left, groups, right), "vcov", paste0("vcov", left, groups, right), "groups", "deleted", "converged", "function.evals") result <- list(fixed, fixed.1, vc, vc.1, .vcov(model), vcov.1, groups, unique.del, converged, feval) names(result) <- nms ## call this influence.merMod since all methods are currently written for it class(result) <- "influence.merMod" result } glmmTMB/inst/other_methods/lsmeans_methods.R0000644000176200001440000000301313614324717020707 0ustar liggesusers## EXPERIMENTAL (not yet exported) #' @importFrom stats delete.response model.frame na.pass recover.data.glmmTMB <- function(object, ...) { fcall <- getCall(object) lsmeans::recover.data(fcall,delete.response(terms(object)), attr(model.frame(object),"na.action"), ...) } lsm.basis.glmmTMB <- function (object, trms, xlev, grid, vcov., mode = "asymptotic", component="cond", ...) { if (mode != "asymptotic") stop("only asymptotic mode is available") if (component != "cond") stop("only tested for conditional component") if (missing(vcov.)) V <- as.matrix(vcov(object)[[component]]) else V <- as.matrix(.my.vcov(object, vcov.)) dfargs = misc = list() if (mode == "asymptotic") { dffun = function(k, dfargs) NA } ## use this? misc = .std.link.labels(family(object), misc) contrasts = attr(model.matrix(object), "contrasts") m = model.frame(trms, grid, na.action = na.pass, xlev = xlev) X = model.matrix(trms, m, contrasts.arg = contrasts) bhat = fixef(object)[[component]] if (length(bhat) < ncol(X)) { kept = match(names(bhat), dimnames(X)[[2]]) bhat = NA * X[1, ] bhat[kept] = fixef(object)[[component]] modmat = model.matrix(trms, model.frame(object), contrasts.arg = contrasts) nbasis = estimability::nonest.basis(modmat) } else nbasis = estimability::all.estble list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun, dfargs = dfargs, misc = misc) } glmmTMB/inst/CITATION0000644000176200001440000000626013614324717013677 0ustar liggesusersbibentry(bibtype = "Article", author = c(person(given = c("Mollie", "E."), family = "Brooks"), person(given = "Kasper", family = "Kristensen"), person(given = c("Koen", "J."), family = c("van", "Benthem")), person(given = "Arni", family = "Magnusson"), person(given = c("Casper", "W."), family = "Berg"), person(given = "Anders", family = "Nielsen"), person(given = c("Hans", "J."), family = "Skaug"), person(given = "Martin", family = "Maechler"), person(given = c("Benjamin", "M."), family = "Bolker") ), title = "{glmmTMB} Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling", year = "2017", journal = "The R Journal", url = "https://journal.r-project.org/archive/2017/RJ-2017-066/index.html", pages = "378--400", volume = "9", number = "2", header = "To cite glmmTMB in publications use:", textVersion = "Mollie E. Brooks, Kasper Kristensen, Koen J. van Benthem, Arni Magnusson, Casper W. Berg, Anders Nielsen, Hans J. Skaug, Martin Maechler and Benjamin M. Bolker (2017). glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. The R Journal, 9(2), 378-400." ) ## bibentry(bibtype = "Article", ## author = "Brooks, Mollie E. and Kristensen, Kasper and van Benthem, Koen J. and Magnusson, Arni and Berg, Casper W. and Nielsen, Anders and Skaug, Hans J. and Maechler, Martin and Bolker, Benjamin M.", ## title = "Modeling Zero-Inflated Count Data With glmmTMB", ## year = "2017", ## url = "http://biorxiv.org/content/early/2017/05/01/132753", ## eprint = "http://biorxiv.org/content/early/2017/05/01/132753.full.pdf", ## journal = "bioRxiv preprint bioRxiv:132753", ## archivePrefix = "bioRxiv", ## header = "To cite glmmTMB in publications use one or both of the following:", ## textVersion = "Mollie E. Brooks, Kasper Kristensen, Koen J. van Benthem, Arni Magnusson, Casper W. Berg, Anders Nielsen, Hans J. Skaug, Martin Maechler, Benjamin M. Bolker (2017). Modeling Zero-Inflated Count Data With glmmTMB. bioRxiv preprint bioRxiv:132753; doi: https://doi.org/10.1101/132753" ## ) ## bibentry(bibtype = "Manual", ## title = "{glmmTMB}: Generalized Linear Mixed Models using Template Model Builder", ## author = "Arni Magnusson and Hans J. Skaug and Anders Nielsen and Casper W. Berg and Kasper Kristensen and Martin Maechler and Koen J. van Bentham and Benjamin M. Bolker and Mollie E. Brooks", ## year = "2017", ## textVersion = "Arni Magnusson, Hans J. Skaug, Anders Nielsen, Casper W. Berg, Kasper Kristensen, Martin Maechler, Koen J. van Bentham, Benjamin M. Bolker and Mollie E. Brooks (2017). glmmTMB: Generalized Linear Mixed Models using Template Model Builder. R package version 0.1.3. https://github.com/glmmTMB" ## ) glmmTMB/inst/example_files/0000755000176200001440000000000013614324717015353 5ustar liggesusersglmmTMB/inst/example_files/salamander_prof1.rds0000644000176200001440000000517013614324717021306 0ustar liggesusersViTPvD (Av0VF n`dU`Xª:jB9`ҢAϰLEXsd2m7||w6]&iy!`m#4`WHre[4!v:{ʸ-]KT=VOwaCa%U^y.\> J^L(,/j] -"lW|BXw5Uc?Ixh-8_Pkxe_v>|>&|r®=z#"8Pҽ!Nk518meAb)"7G$KzJwҢX '8v;s~z5cyݥkyft'Wtkrlθ*\(ӪT xT BSXylP&J *yW HvYViaxWKm=zI1嬕?ṢGn`|$j1SOr{#/g(3t{xܜ1YR5J"GHg\viIHw5I^->t4A= U]Tr Au|jnJ_g|j黲*|JQ'P2?3 0d`gC_?>t=C';J|^8ֵVV۠\Rg(0Q9m/Jm?o;ȕuԼ)|JŻo[g}Ct>kdߍ|hSLQ~֫(ģ|: _G~=@׃6Q`d$:q%7W7 `g]NDUCFצ#_NW/R q3{ot-6k(MCӋ9JPpʋkE6K?E>?W[ z/fW?~Ps.A֤x"O~uѾT#u앻գvhRr3d]9[|TGYv8)8\8MU7}J57DRA;/P*M[ stJB}X5*C}|QJձ]]l>3ǝ3FF9:?yR\;7Jk Q}z2 +Q=!{tΚ)}&=}/d^W]Ԇ=ιWZ]nn^͈JwOcH>wRWmA5ϝP $;Bc͓,EC.",D:Nʛ k kY1ȒA 2gA$$eA2$AJ8XLRBƒ0$,fyM% Β, 0IIt6ךdZ`h!CC3`HF2b٤%N,ㅡ%-^!HHɐ%Rz5 CK -04ǐ! 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ɅdHա]!Vn#kNoUTdڐΪLE%l󚣛P1JV6DS~:W _@\^7hj7y\iʱRIBNz.( D6ޢ4s-G8(X*h{*E%ġO- +fG,;e|`J6|ڍZP& )^l1*^i&-b+УX2YeyzcP$Vs}e GTA3(8BF1 Es8J*A hz ;,pdd*^O1Wwڋ B6K@G̫c~d!rPjuY JBI}!n(C"Һr%iNAKSøB5S[T»mLQ|<-ti>`oM@?1K쀁d4u)r K8`Ӯ{gc2YϺD9j ?Gh^>;'y|.%"8qvoK㙝fX^OQq ~XRl=?f cdjnnq>j#aBL:T't~a.nc³1~7yzy؞qru>sӾ^܍﵀=wm\=B?[:,;uNh|?.K`}%<y9glmmTMB/inst/vignette_data/mcmc.R0000644000176200001440000000642013614324717016420 0ustar liggesusers## @knitr run_MCMC ##' @param start starting value ##' @param V variance-covariance matrix of MVN candidate distribution ##' @param iterations total iterations ##' @param nsamp number of samples to store ##' @param burnin number of initial samples to discard ##' @param thin thinning interval ##' @param tune tuning parameters; expand/contract V ##' @param seed random-number seed run_MCMC <- function(start, V, logpost_fun, iterations = 10000, nsamp = 1000, burnin = iterations/2, thin = floor((iterations-burnin)/nsamp), tune = NULL, seed=NULL ) { ## initialize if (!is.null(seed)) set.seed(seed) if (!is.null(tune)) { tunesq <- if (length(tune)==1) tune^2 else outer(tune,tune) V <- V*tunesq } chain <- matrix(NA, nsamp+1, length(start)) chain[1,] <- cur_par <- start postval <- logpost_fun(cur_par) j <- 1 for (i in 1:iterations) { proposal = MASS::mvrnorm(1,mu=cur_par, Sigma=V) newpostval <- logpost_fun(proposal) accept_prob <- exp(newpostval - postval) if (runif(1) < accept_prob) { cur_par <- proposal postval <- newpostval } if ((i>burnin) && (i %% thin == 1)) { chain[j+1,] <- cur_par j <- j + 1 } } chain <- na.omit(chain) colnames(chain) <- names(start) chain <- coda::mcmc(chain) return(chain) } ## @knitr pkgs library(glmmTMB) library(tmbstan) library(coda) ## @knitr fit1 data("sleepstudy",package="lme4") fm1 <- glmmTMB(Reaction ~ Days + (Days|Subject), sleepstudy) ## @knitr setup ## FIXME: is there a better way for user to extract full coefs? rawcoef <- with(fm1$obj$env,last.par[-random]) names(rawcoef) <- make.names(names(rawcoef),unique=TRUE) ## log-likelihood function ## (MCMCmetrop1R wants *positive* log-lik) logpost_fun <- function(x) -fm1$obj$fn(x) ## check definitions stopifnot(all.equal(c(logpost_fun(rawcoef)), c(logLik(fm1)), tolerance=1e-7)) V <- vcov(fm1,full=TRUE) ## @knitr do_run_MCMC t1 <- system.time(m1 <- run_MCMC(start=rawcoef, V=V, logpost_fun=logpost_fun, seed=1001)) ## @knitr do_tmbstan ## install.packages("tmbstan") library(tmbstan) t2 <- system.time(m2 <- tmbstan(fm1$obj)) ## @knitr stanhacks ## functions to reduce the size of stored Stan-type objects hack_size <- function(x, ...) { UseMethod("hack_size") } hack_size.stanfit <- function(x) { x@stanmodel <- structure(numeric(0), class="stanmodel") x@.MISC <- new.env() return(x) } hack_size.brmsfit <- function(x) { x$fit <- hack_size(x$fit) return(x) } hack_size.stanreg <- function(x) { x$stanfit <- hack_size(x$stanfit) return(x) } m2 <- hack_size(m2) ## @knitr tmbstan_traceplot png("tmbstan_traceplot.png") rstan::traceplot(m2, pars=c("beta","betad","theta")) dev.off() ## @knitr save_all ## use version=2 to allow compatibility pre-3.5.0 ## DON'T save m2; even with size-hacking, not small enough. ## since PNG file is saved, we don't really need it save("m1","t1","t2", file="mcmc.rda", version=2) glmmTMB/inst/NEWS.Rd0000644000176200001440000001424513616054060013600 0ustar liggesusers\newcommand{\PR}{\Sexpr[results=rd]{tools:::Rd_expr_PR(#1)}} \name{NEWS} \title{glmmTMB News} \encoding{UTF-8} \section{CHANGES IN VERSION 1.0.0}{ The 1.0.0 release does not introduce any major changes or incompatibilities, but signifies that glmmTMB is considered stable and reliable for general use. \subsection{NEW FEATURES}{ \itemize{ \item new \code{map} argument to \code{glmmTMB} allows for some parameter values to be fixed (see \code{?TMB::MakeADFun} for details) \item new \code{optimizer} and \code{optArgs} arguments to \code{glmmTMBControl} allow use of optimizers other than \code{nlminb} \item \code{predict} can make population-level predictions (i.e., setting all random effects to zero). See \code{?predict.glmmTMB} for details. \item \code{beta_family} now allows zero-inflation; new \code{ziGamma} family (minor modification of \code{stats::Gamma}) allows zero-inflation (i.e., Gamma-hurdle models) } } % new features \subsection{BUG FIXES}{ \itemize{ \item \code{vcov(., full=TRUE)} (and hence profiling) now work for models with \code{dispformula=~0} \item Documentation fix: when \code{family=genpois}, the index of dispersion is known as phi^2. \item \code{Anova} now respects the \code{component} argument (GH #494, from @eds-slim) \item \code{predict} now works when contrasts are set on factors in original data (GH #439, from @cvoeten) \item \code{bootMer} now works with models with Bernoulli responses (even though \code{simulate()} returns a two-column matrix in this case) (GH #529, @frousseu) \item better support for \code{emmeans} applied to zero-inflation or dispersion models (correct link functions) (Russ Lenth) } } % bug fixes \subsection{USER-VISIBLE CHANGES}{ \itemize{ \item \code{sigma(.)} now returns \code{NA} for models with non-trivial dispersion models (i.e. models with more than one dispersion parameter) (raised by GH #533, from @marek-tph) \item \code{VarCorr} no longer prints residual variances for models with \code{dispformula=~0} \item the \code{model.matrix()} and \code{terms()} methods for \code{glmmTMB} objects have been slightly modified } } % user-visible changes } % version 1.0.0 \section{CHANGES IN VERSION 0.2.3}{ \subsection{NEW FEATURES}{ \itemize{ \item \code{ranef} now returns information about conditional variances (as attributes of the individual random effects terms) by default; this information can easily be retrieved by \code{as.data.frame(ranef(.))}. \item \code{coef} method now available: as in \code{lme4}, returns sum of fixed + random effects for each random-effects level. (Conditional variances for \code{coef} \emph{not} yet available.) \item simulate works for models with genpois family \item parametric bootstrapping should work, using \code{\link[lme4]{bootMer}} from the \code{lme4} package as a front end. } % itemize } % new features \subsection{BUG FIXES}{ \itemize{ \item models with multiple types of RE (e.g. ar1 and us) may have failed previously (GH #329) \item \code{predict} was not handling data-dependent predictors (e.g. \code{poly}, \code{spline}, \code{scale}) correctly \item \code{profile} now works for models without random effects } } % bug fixes \subsection{USER-VISIBLE CHANGES}{ \itemize{ \item The value returned from \code{simulate} for binomial models is now a non-standard data frame where each element contains a two-column matrix (as in the base-R \code{\link{simulate}} method for binomial GLMS). } % itemize } % user-visible } % version 0.2.3 \section{CHANGES IN VERSION 0.2.2}{ \subsection{NEW FEATURES}{ \itemize{ \item REML is now an option (GH #352). It is typically only for Gaussian response variables, but can also be useful for some non-Gaussian response variables if used with caution (i.e. simulate a test case first). } } \subsection{USER-VISIBLE CHANGES}{ \itemize{ \item Because family functions are now available for all families that have been implemented in the underlying TMB code, specifying the \code{family} argument as a raw list (rather than as a family function, the name of a family function, or the output of such a function) is now deprecated. } } } \section{CHANGES IN VERSION 0.2.1}{ \subsection{NEW FEATURES}{ \itemize{ \item likelihood profiles (via \code{profile}) and likelihood profile confidence intervals (via \code{confint(profile(.))}) can now be computed; \code{confint(fitted,method="profile")} and \code{confint(fitted,method="uniroot")} (find CIs by using a root-finding algorithm on the likelihood profile) \item offsets are now allowed in the zero-inflation and dispersion formulas as well as in the main (conditional-mean) formula (if \code{offset} is specified as a separate argument, it applies only to the conditional mean) \item zero-truncated generalized Poisson \code{family=truncated_genpois} \item zero-truncated Conway-Maxwell-Poisson \code{family=truncated_compois} \item \code{predict} now allows \code{type} ("link", "response", "conditional", "zprob", "zlink") } } \subsection{BUG FIXES}{ \itemize{ \item built-in \code{betar()} family for Beta regression fixed (and name changed to \code{beta_family()}) (GH #278) \item fixed segfault in predict method when response is specified as two columns (GH #289) \item fixed summary-printing bug when some random effects have covariance terms and others don't (GH #291) \item fix bugs in binomial residuals and prediction (GH #307) } } \subsection{USER-VISIBLE CHANGES}{ \itemize{ \item in \code{predict.glmmTMB}, the \code{zitype} argument has been rolled into the new \code{type} argument: \strong{default prediction type is now "link" instead of "response", in order to match \code{glm()} default} } } } glmmTMB/cleanup0000755000176200001440000000004613616062000013120 0ustar liggesusers#!/bin/sh rm -f config.* src/Makevars

Warnings

Model convergence problem; non-positive-definite Hessian matrix; NA values for likelihood/AIC/etc.

This warning (Model convergence problem; non-positive-definite Hessian matrix) states that at glmmTMB’s maximum-likelihood estimate, the curvature of the negative log-likelihood surface is inconsistent with glmmTMB really having found the best fit (minimum): instead, the surface is downward-curving, or flat, in some direction(s).

It will usually be accompanied by NA values for the standard errors, log-likelihood, AIC, and BIC, and deviance. When you run summary() on the resulting model, you’ll get the warning In sqrt(diag(vcov)) : NaNs produced.

These problems are most likely:

  • when a model is overparameterized (i.e. the data does not contain enough information to estimate the parameters reliably)
  • when a random-effect variance is estimated to be zero, or random-effect terms are estimated to be perfectly correlated (“singular fit”: often caused by having too few levels of the random-effect grouping variable)
  • when zero-inflation is estimated to be near zero (a strongly negative zero-inflation parameter)
  • when dispersion is estimated to be near zero
  • when complete separation occurs in a binomial model: some categories in the model contain proportions that are either all 0 or all 1

How do we diagnose the problem?

Example 1.

Consider this example:

zinbm0 = glmmTMB(count~spp + (1|site), zi=~spp, Salamanders, family=nbinom2)

First, see if any of the estimated coefficients are extreme. If you’re using a non-identity link function (e.g. log, logit), then parameter values with \(|\beta|>10\) are suspect (for a logit link, this implies probabilities very close to 0 or 1; for a log link, this implies mean counts that are close to 0 or extremely large).

Inspecting the fixed-effect estimates for this model:

fixef(zinbm0)

The zero-inflation intercept parameter is tiny (\(\approx -17\)): since the parameters are estimated on the logit scale, we back-transform with plogis(-17) to see the at the zero-inflation probability for the baseline level is about \(4 \times 10^{-8}\))). Many of the other ZI parameters are very large, compensating for the intercept: the estimated zero-inflation probabilities for all species are

ff <- fixef(zinbm0)$zi
round(plogis(c(sppGP=unname(ff[1]),ff[-1]+ff[1])),3)

Since the baseline probability is already effectively zero, making the intercept parameter larger or smaller will have very little effect - the likelihood is flat, which leads to the non-positive-definite warning.

Now that we suspect the problem is in the zero-inflation component, we can try to come up with ways of simplifying the model: for example, we could use a model that compared the first species (“GP”) to the rest:

Salamanders <- transform(Salamanders, GP=as.numeric(spp=="GP"))
zinbm0_A = update(zinbm0, ziformula=~GP)

This fits without a warning, although the GP zero-inflation parameter is still extreme:

fixef(zinbm0_A)[["zi"]]

Another possibility would be to fit the variation among species in the zero-inflation parameter as a random effect, rather than a fixed effect: this is slightly more parsimonious. This again fits without an error, although both the average level of zero-inflation and the among-species variation are estimated as very small:

zinbm0_B = update(zinbm0, ziformula=~(1|spp))
fixef(zinbm0_B)[["zi"]]
VarCorr(zinbm0_B)

The original analysis considered variation in zero-inflation by site status (mined or not mined) rather than by species - this simpler model only tries to estimate two parameters (mined + difference between mined and no-mining) rather than 7 (one per species) for the zero-inflation model.

zinbm1 = glmmTMB(count~spp + (1|site), zi=~mined, Salamanders, family=nbinom2)
fixef(zinbm1)[["zi"]]

This again fits without a warning, but we see that the zero-inflation is effectively zero in the unmined (“minedno”) condition (plogis(0.38-17.5) is approximately \(4 \times 10^{-8}\)). We can estimate the confidence interval, but it takes some extra work: the default Wald standard errors and confidence intervals are useless in this case.

## at present we need to specify the parameter by number; for
##  extreme cases need to specify the parameter range
## (not sure why the upper bound needs to be so high ... ?)
cc = confint(zinbm1,method="uniroot",parm=9, parm.range=c(-20,20))
print(cc)

The lower CI is not defined; the upper CI is -2.08, i.e. we can state that the zero-inflation probability is less than plogis(-2.08) = 0.11.

More broadly, general inspection of the data (e.g., plotting the response against potential covariates) should help to diagnose overly complex models.

Example 2.

In some cases, scaling predictor variables may help. For example, in this example from @phisanti, the results of glm and glmmTMB applied to a scaled version of the data set agree, while glmmTMB applied to the raw data set gives a non-positive-definite Hessian warning.

## data taken from gamlss.data:plasma, originally
## http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/plasma.html
load(system.file("vignette_data","plasma.rda", package="glmmTMB"))
m4.1 <- glm(calories ~ fat*fiber, family = Gamma(link = "log"), data = plasma)
m4.2 <- glmmTMB(calories ~ fat*fiber, family = Gamma(link = "log"), data = plasma)
ps  <- transform(plasma,fat=scale(fat,center=FALSE),fiber=scale(fiber,center=FALSE))
m4.3 <- update(m4.2, data=ps)
## scaling factor for back-transforming standard deviations
ss <- c(1,
        fatsc <- 1/attr(ps$fat,"scaled:scale"),
        fibsc <- 1/attr(ps$fiber,"scaled:scale"),
        fatsc*fibsc)
## combine SEs, suppressing the warning from the unscaled model
s_vals <- cbind(glm=sqrt(diag(vcov(m4.1))),
                glmmTMB_unsc=suppressWarnings(sqrt(diag(vcov(m4.2)$cond))),
                glmmTMB_sc=sqrt(diag(vcov(m4.3)$cond))*ss)
print(s_vals,digits=3)

Example 3.

Here is another example (from Samantha Sherman):

load(system.file("vignette_data","troubleshooting.rda",package="glmmTMB"))

The first model gives the specified warning when it runs, as well as the other symptoms such as NA values for the likelihood:

summary(mod1)

We can immediately see that the dispersion is very small and that the zero-inflation parameter is strongly negative. However, we’ll develop some fancier machinery that checks the variance-covariance matrix or Hessian of the model, finds eigenvalues that are negative or close to zero, and identifies which model components contribute to those eigenvalues:

diagnose_vcov <- function(model, tol=1e-5, digits=2, analyze_hessian=FALSE) {
    vv <- vcov(model, full=TRUE)
    nn <- rownames(vv)
    if (!all(is.finite(vv))) {
        if (missing(analyze_hessian)) warning("analyzing Hessian, not vcov")
        if (!analyze_hessian) stop("can't analyze vcov")
        analyze_hessian <- TRUE
    }
    if (analyze_hessian) {
        par.fixed <- model$obj$env$last.par.best
        r <- model$obj$env$random
        if (!is.null(r)) par.fixed <- par.fixed[-r]
        vv <- optimHess(par.fixed, fn=model$obj$fn, gr=model$obj$gr)
        ## note vv is now HESSIAN, not vcov
    }
    ee <- eigen(vv)
    if (all(ee$values>tol)) {message("var-cov matrix OK"); return(invisible(NULL))}
    ## find negative or small-positive eigenvalues (flat/wrong curvature)
    bad_evals <- which(ee$values<tol)
    ## order worst to best
    bad_evals <- bad_evals[order(-ee$values[bad_evals])]
    ret <- lapply(bad_evals,
                  function(i) {
                      ## extract loadings
                      v <- setNames(ee$vectors[,i], nn)
                      ## order in decreasing magnitude & round
                      list(val=ee$values[i],vec=round(v[order(-abs(v))],digits))
                  })
    return(ret)
}

Running the diagnostics on the model:

(d1 <- diagnose_vcov(mod1))

This model has a very bad eigenvalue that is mostly driven by the zero-inflation parameter, and a little bit by the dispersion parameter. Let’s try dropping the zero-inflation term:

mod2 <- update(mod1, ziformula=~0)
summary(mod2)

We still get the warning, and the NA-valued likelihoods (and the very small dispersion parameter). Diagnose:

diagnose_vcov(mod2)

We can see that the dispersion parameter is still problematic. Simplify the model by switching from NB1 to Poisson:

mod3 <- update(mod2, family=poisson)
summary(mod3)

There are no warnings, the model looks OK now, and the diagnostic function agrees:

diagnose_vcov(mod3)

You can also check directly whether the model is OK by examining the pdHess (“positive-definite Hessian”) component of the sdr (“standard deviation report”) component of the model:

mod3$sdr$pdHess

(FIXME: add an accessor method for this?)

In general models with non-positive definite Hessian matrices should be excluded from further consideration.

Model convergence problem: eigenvalue problems

m1 = glmmTMB(count~spp + mined + (1|site), zi=~spp + mined, Salamanders, family=genpois)

In this example, the fixed-effect covariance matrix is NaN. It may have to do with the generalized Poisson (genpois) distribution, which is known to have convergence problems; luckily, the negative binomial (nbinom1 and nbinom2) and/or Conway-Maxwell Poisson (compois) are good alternatives.

Models with convergence problems should be excluded from further consideration, in general.

In some cases, extreme eigenvalues may be caused by having predictor variables that are on very different scales: try rescaling, and centering, continuous predictors in the model.

NA/NaN function evaluation

Warning in nlminb(start = par, objective = fn, gradient = gr) : NA/NaN function evaluation

This warning occurs when the optimizer visits a region of parameter space that is invalid. It is not a problem as long as the optimizer has left that region of parameter space upon convergence, which is indicated by an absence of the model convergence warnings described above.

The following warnings indicate possibly-transient numerical problems with the fit, and can be treated in the same way (i.e. ignored if there are no errors or convergence warnings about the final fitted model).

Cholmod warning ‘matrix not positive definite’

In older versions of R (< 3.6.0):

Warning in f(par, order = order, …) : value out of range in ‘lgamma’

false convergence

This warning:

false convergence: the gradient ∇f(x) may be computed incorrectly, the other stopping tolerances may be too tight, or either f or ∇f may be discontinuous near the current iterate x

comes from the nlminb optimizer used by default in glmmTMB. It’s usually hard to diagnose the source of this warning (this Stack Overflow answer explains a bit more about what it means). Reasonable methods for making sure your model is OK are:

  • restart the model at the estimated fitted values
  • try using a different optimizer, e.g. control=glmmTMBControl(optimizer=optim, optArgs=list(method="BFGS"))

and see if the results are sufficiently similar to the original fit.