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liggesusersmutoss/R/localfdr.R0000644000176200001440000000604015123457163013725 0ustar liggesusers# # Author: JonathanRosenblatt ############################################################################### pval2qval<- function(pValues, cutoff){ requireLibrary('fdrtool') fdrtool <- get("fdrtool", envir=asNamespace("fdrtool")) qvals<-fdrtool( pValues, statistic= 'pvalue', plot=FALSE,verbose=FALSE)$qval if (missing(cutoff)) { return(list(qValues=qvals)) } return(list(qValues=qvals, rejected= qvals<=cutoff )) } pval2locfdr<- function(pValues, cutoff){ requireLibrary('fdrtool') fdrtool <- get("fdrtool", envir=asNamespace("fdrtool")) locfdr<-fdrtool( pValues, statistic= 'pvalue', plot=FALSE,verbose=FALSE)$lfdr if (missing(cutoff)) { return(list(locFDR=locfdr)) } return(list(locFDR=locfdr, rejected= locfdr<=cutoff )) } mutoss.locfdr <- function() { return(new(Class="MutossMethod", label="Local FDR (fdr)", callFunction="pval2locfdr", output=c("locFDR", "rejected"), info= "

Name:

Local fdr.\n

Also known as:

fdr, empirical posterior probability of the null. \n

Error Type:

Motivated by Bayesian considerations. Does not guarantee control of frequentist error types like FWER or FDR.\n

Recommended Usage:

Typically used when a massive amount of hypotheses is being tested as in microarray analyses.\n

Related procedures:

See FDR methods for similar procedures for frequentist error control.\n

References:

\n ", parameters=list( pValues=list(type="numeric"), cutoff=list(type="numeric", label="Local fdr cutoff for rejection", optional=TRUE)))) } mutoss.qvalues <- function() { return(new(Class="MutossMethod", label="q Values (Fdr)", callFunction="pval2qval", output=c("qValues", "rejected"), info= "

Name:

q-Values.\n

Also known as:

\n ", parameters=list(pValues=list(type="numeric"), alpha=list(type="numeric")) )) } mutoss/R/CompareObjects.R0000644000176200001440000001736515123457163015053 0ustar liggesusers# # Author: JonathanRosenblatt ############################################################################### #----------- Validating input of compare procedure------------# mu.test.class<- function(classes){ if (any(classes!='Mutoss')) stop('Input is not a "Motoss" class object') ##TODO: will this cause prolems for inherited class objects? } mu.test.type<- function(types){ if( any(types != types[1]) ){ message(' Notice:You are comparing methods for different error types. \n These should not be compared! \n Output will be generated nevertheless. \n') } } mu.test.rates<-function(rates){ if( any(rates != rates[1]) ){ message(' Notice:You are comparing methods with different error rates. \n These should not be compared! \n Output will be generated nevertheless. \n') } } mu.test.same.data<- function(pvals){ pvals.different<- any( apply(pvals,1, function(x) any(x!=x[1]))) if(pvals.different) stop('Different data was used for suppied procedures.') } mu.test.name<- function(hyp.names){ names.different<- any( apply(hyp.names,1, function(x) any(x!=x[1]))) if(names.different) message('Notice: Hypotheses have different names. Can they be compared?') } #-------- Create comparison list for Mutoss objects--------------# compareMutoss<-function(...){ objects<-list(...) classes<- sapply(objects, function(x) class(x) )#getting object classes mu.test.class(classes) #testing for compatible object classes types<- sapply(objects, function(x) x@errorControl@type)#getting error control types mu.test.type(types) #testing for compatible error control types rates<-sapply(objects, function(x) x@errorControl@alpha)#extracting error rates pi.nulls<- as.numeric(lapply(objects, function(x) x@pi0))#extracting pi0 estimates pvalues<- sapply(objects, function(x) x@pValues )# getting adjusted pvals mu.test.same.data(pvalues) m<- nrow(pvalues) raw.hyp.names<- lapply(objects, function(x) x@hypNames )# getting hypothesis names if(all(sapply(raw.hyp.names, function(x) identical(x, character(0))))){ hyp.names<- paste('hyp', 1:m, sep='') } else if(all(sapply(raw.hyp.names, function(x) length(x)==m))) { mu.test.name(raw.hyp.names) hyp.names<- raw.hyp.names[,1] } ##TODO: [JR] Deal with mising hyp names only in a subset of objects # Preparing Raw Pvalues raw.pvals<- pvalues[,1] pval.order<- order(raw.pvals) pval.ranks<- rank(raw.pvals) raw.pvals.frame<-data.frame( pValue=raw.pvals, order=pval.order, ranks=pval.ranks) row.names(raw.pvals.frame)<-hyp.names #Preparing adjusted pvalues adj.pvals<- lapply(objects, function(x) x@adjPValues ) method.names<- unlist(lapply(adj.pvals, function(x) attributes(x)[1])) adj.pvals.frame<- data.frame(adj.pvals) colnames(adj.pvals.frame)<- method.names row.names(adj.pvals.frame)<- hyp.names #Preparing critical values critical<- lapply(objects, function(x) x@criticalValues ) method.names<- unlist(lapply(critical, function(x) attributes(x)[1])) critical.frame<- data.frame(critical) colnames(critical.frame)<- method.names row.names(critical.frame)<- hyp.names #Preparing decisions rejections<- lapply(objects, function(x) x@rejected ) method.names<- unlist(lapply(rejections, function(x) attributes(x)[1])) rejections.frame<- data.frame(rejections) colnames(rejections.frame)<- method.names row.names(rejections.frame)<- hyp.names ##TODO: [JR] Add groud truth to comparison method comparing<- list( types=types, rates=rates, pi.nulls=pi.nulls, raw.pValues=raw.pvals.frame, adjusted.pvals=adj.pvals.frame, criticalValue=critical.frame, rejections=rejections.frame ) return(comparing) } #For testing purposes #source('~/workspace/mutoss/src/BasicFunctions/DummyBigObjects.R') #test<- list(mu.test.obj.1, mu.test.obj.2) #-------------- Comparison of adjusted p values -----------------# mu.compare.adjusted<- function(comparison.list, identify.check=F){ adjPValues<- comparison.list[['adjusted.pvals']] hyp.num<- nrow(adjPValues) method.num<- ncol(adjPValues) method.names<- factor(colnames(adjPValues)) method.index<- as.numeric(method.names) raw.pValues<-comparison.list[['raw.pValues']] pvalue.ranks<-raw.pValues$ranks method.type<-comparison.list[['types']] stacked.adjPValues<- unlist(adjPValues, use.names=F) x<- rep(pvalue.ranks, method.num) method.labels<- rep(method.names, each=hyp.num) hyp.labels<- rep(row.names(adjPValues), method.num) point.charachters<- rep(method.index, each=hyp.num) #for plotting purposes only point.size<- hyp.num^(-0.1) plot(stacked.adjPValues~x, pch=point.charachters, ylim=c(0,1), cex=point.size, xlab='') the.title<- paste('Adjusted p-values for ',unique(method.type),' controlling procedures') title(the.title) par(xpd=T) legend(x=0, y=-0.15, horiz=T, legend=method.names, pch=method.index, cex=method.num ^ (-1/4) ) par(xpd=F) #reset par to default value if(identify.check) { identify(stacked.adjPValues~x, labels=hyp.labels ) } ##TODO: [JR] Plotting method using colors? } #For testing purposes #source('~/workspace/mutoss/src/BasicFunctions/DummyBigObjects.R') #----------- Comparison of critical vales---------- # mu.compare.critical<- function(comparison.list, identify.check=F){ method.type<-comparison.list[['types']] #extracting method type criticalValues<- comparison.list[['criticalValue']]#extracting critical values hyp.num<- nrow(criticalValues) method.num<- ncol(criticalValues) method.names<- factor(colnames(criticalValues)) method.index<- as.numeric(method.names) raw.pValues<-comparison.list[['raw.pValues']] #extracting raw palues pvalue.ranks<-raw.pValues$ranks stacked.criticalValues<- unlist(criticalValues, use.names=F) x<- rep(pvalue.ranks, method.num) method.labels<- rep(method.names, each=hyp.num) hyp.labels<- rep(row.names(criticalValues), method.num) point.charachters<- rep(method.index, each=hyp.num) #for plotting purposes only point.size<- hyp.num^(-0.1) plot(stacked.criticalValues~x, pch=point.charachters, ylim=c(0,1), xlab='', cex=point.size) the.title<- paste('Critical Values for ',unique(method.type),' controlling procedures') title(the.title) par(xpd=T) legend( x=0, y=-0.15, horiz=T, legend=method.names, pch=method.index, cex=method.num ^ (-1/4) ) par(xpd=F) #reset par to default value if(identify.check) { identify(stacked.criticalValues~x, labels=hyp.labels ) } } #For testing purposes: #source('~/workspace/mutoss/src/BasicFunctions/DummyBigObjects.R') #mu.compare.critical(1) #mu.compare.critical(compare.3, T) #----- Sumary of comparison-----------# mu.compare.summary<- function(comparison.list){ method.type<-comparison.list[['types']] #extracting method type error.rates<-comparison.list[['rates']] #extracting error rates pi.nulls<-comparison.list[['pi.nulls']] #extracting pi0 rejections<-comparison.list[['rejections']] hyp.num<- nrow(rejections) method.num<- ncol(rejections) method.names<- factor(colnames(rejections)) method.index<- as.numeric(method.names) count.rejections<-apply(rejections, 2, sum) seperate<- rep('|', method.num) summary<-data.frame( method.type, seperate, error.rates, seperate, count.rejections, seperate, pi.nulls) colnames(summary) <-c( 'Error Type'," ", 'Error Rate'," ", 'Rejections Count', " ", 'pi_0') cat('\n Comparing multipe hypothesis procedures.\n', hyp.num, 'hypotheses tested.\n\n') print(summary) } #For testing purposes : #source('~/workspace/mutoss/src/BasicFunctions/DummyBigObjects.R') #mu.compare.summary(1) #mu.compare.summary(compare.3)mutoss/R/helperfunctions.R0000644000176200001440000000061615123457163015352 0ustar liggesusers requireLibrary <- function(package) { if(!require(package, character.only=TRUE)) { answer <- readline(paste("Package ",package," is required - should we install it?",sep="")) if (substr(answer, 1, 1) %in% c("y","Y")) { install.packages(package) require(package, character.only=TRUE) } else { stop(paste("Required package",package,"should not be installed")) } } } mutoss/R/mutossApply.R0000644000176200001440000000225415123457163014502 0ustar liggesusersmutoss.apply <- function(mutossObj, f, label = deparse(substitute(f)), recordHistory = TRUE, ...) { params <- list() for (param in names(formals(f))) { #runs over all parameters of f if (param %in% slotNames(mutossObj)) { #checks if parameter name corresponds to a mutoss slot paramTail <- list(slot(mutossObj, param)) #extracts values from appropriate slot names(paramTail) <- param #attaches names for parameter values params <- c(params, paramTail) #concatenates parameters with and without data from objects } } result <- eval(as.call(c(f,params,...))) #evaluates function call with extracted parameters for (param in names(result)) { if (param %in% slotNames(mutossObj)) { value <- result[param][[1]] attr(value, "method.name") <- label #attaches attributes to slot(mutossObj, param) <- value #writes result in corresponding mutoss slots } } if ( recordHistory ) { mutossObj@commandHistory = c( mutossObj@commandHistory, paste(format( match.call( call = sys.call( sys.parent(1) ))), collapse='')) #write command into history } return(mutossObj) } mutoss/R/nparcomp1.R0000644000176200001440000007520215123457163014045 0ustar liggesusers# Simultaneous confidence intervals for relative contrast effects # # Author: FrankKonietschke ############################################################################### nparcomp <- function (formula, data, type = c("UserDefined", "Tukey", "Dunnett", "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus","UmbrellaWilliams"), control = NULL, conflevel = 0.95, alternative = c("two.sided", "less", "greater"), rounds = 3, correlation = FALSE, asy.method = c("logit", "probit", "normal", "mult.t"), plot.simci = FALSE, info = TRUE, contrastMatrix = NULL) { corr.mat <- function(m, nc) { rho <- matrix(c(0), ncol = nc, nrow = nc) for (i in 1:nc) { for (j in 1:nc) { rho[i, j] <- m[i, j]/sqrt(m[i, i] * m[j, j]) } } return(rho) } ssq <- function(x) { sum(x * x) } logit <- function(p) { log(p/(1 - p)) } probit <- function(p) { qnorm(p) } expit <- function(G) { exp(G)/(1 + exp(G)) } index <- function(char, test) { nc <- length(char) for (i in 1:nc) { if (char[i] == test) { return(i) } } } z.quantile <- function(conflevel = conflevel, corr, a, df = df.sw, dbs) { if (dbs == "n") { if (a == "two.sided") { z <- qmvnorm(conflevel, corr = corr, tail = "both")$quantile } if (a == "less" || a == "greater") { z <- qmvnorm(conflevel, corr = corr, tail = "lower")$quantile } } if (dbs == "t") { if (a == "two.sided") { z <- qmvt(conflevel, df = df.sw, interval = c(-10, 10), corr = corr, tail = "both")$quantile } if (a == "less" || a == "greater") { z <- qmvt(conflevel, df = df.sw, interval = c(-10, 10), corr = corr, tail = "lower")$quantile } } return(z) } if (conflevel >= 1 || conflevel <= 0) { stop("The confidence level must be between 0 and 1!") if (is.null(alternative)) { stop("Please declare the alternative! (two.sided, less, greater)") } } type <- match.arg(type) alternative <- match.arg(alternative) asy.method <- match.arg(asy.method) if (length(formula) != 3) { stop("You can only analyse one-way layouts!") } dat <- model.frame(formula, data) if (ncol(dat) != 2) { stop("Specify one response and only one class variable in the formula") } if (is.numeric(dat[, 1]) == FALSE) { stop("Response variable must be numeric") } response <- dat[, 1] factorx <- as.factor(dat[, 2]) fl <- levels(factorx) a <- nlevels(factorx) if (a <= 2) { stop("You want to perform a two-sample test. Please use the function npar.t.test") } samples <- split(response, factorx) n <- sapply(samples, length) if (any(n <= 1)) { warn <- paste("The factor level", fl[n <= 1], "has got only one observation!") stop(warn) } ntotal <- sum(n) a <- length(n) tmp <- expand.grid(1:a, 1:a) ind <- tmp[[1]] > tmp[[2]] vi <- tmp[[2]][ind] vj <- tmp[[1]][ind] nc <- length(vi) gn <- n[vi] + n[vj] intRanks <- lapply(samples, rank) pairRanks <- lapply(1:nc, function(arg) { rank(c(samples[[vi[arg]]], samples[[vj[arg]]])) }) pd <- sapply(1:nc, function(arg) { i <- vi[arg] j <- vj[arg] (sum(pairRanks[[arg]][(n[i] + 1):gn[arg]])/n[j] - (n[j] + 1)/2)/n[i] }) dij <- dji <- list(0) sqij <- sapply(1:nc, function(arg) { i <- vi[arg] j <- vj[arg] pr <- pairRanks[[arg]][(n[i] + 1):gn[arg]] dij[[arg]] <<- pr - sum(pr)/n[j] - intRanks[[j]] + (n[j] + 1)/2 ssq(dij[[arg]])/(n[i] * n[i] * (n[j] - 1)) }) sqji <- sapply(1:nc, function(arg) { i <- vi[arg] j <- vj[arg] pr <- pairRanks[[arg]][1:n[i]] dji[[arg]] <<- pr - sum(pr)/n[i] - intRanks[[i]] + (n[i] + 1)/2 ssq(dji[[arg]])/(n[j] * n[j] * (n[i] - 1)) }) vd.bf <- ntotal * (sqij/n[vj] + sqji/n[vi]) singular.bf <- (vd.bf == 0) vd.bf[singular.bf] <- 1e-05 df.sw <- (n[vi] * sqij + n[vj] * sqji)^2/((n[vi] * sqij)^2/(n[vj] - 1) + (n[vj] * sqji)^2/(n[vi] - 1)) lambda <- sqrt(n[vi]/(gn + 1)) cov.bf1 <- diag(nc) rho.bf <- diag(nc) for (x in 1:(nc - 1)) { for (y in (x + 1):nc) { i <- vi[x] j <- vj[x] v <- vi[y] w <- vj[y] p <- c(i == v, j == w, i == w, j == v) if (sum(p) == 1) { cl <- list(function() (t(dji[[x]]) %*% dji[[y]])/(n[j] * n[w] * n[i] * (n[i] - 1)), function() (t(dij[[x]]) %*% dij[[y]])/(n[i] * n[v] * n[j] * (n[j] - 1)), function() -(t(dji[[x]]) %*% dij[[y]])/(n[v] * n[j] * n[i] * (n[i] - 1)), function() -(t(dij[[x]]) %*% dji[[y]])/(n[i] * n[w] * n[j] * (n[j] - 1))) case <- (1:4)[p] rho.bf[x, y] <- rho.bf[y, x] <- sqrt(ntotal * ntotal)/sqrt(vd.bf[x] * vd.bf[y]) * cl[[case]]() cov.bf1[x, y] <- cov.bf1[y, x] <- sqrt(vd.bf[x] * vd.bf[y]) } } } V <- (cov.bf1 + diag(vd.bf - 1)) * rho.bf cov.bf1 <- cbind(V,-1*V) cov.bf2<-cbind(-1*V,V) cov.bf <- rbind(cov.bf1,cov.bf2) switch(type, UserDefined = { if (is.null(contrastMatrix)){stop("Give a contrast matrix with the contrast.matrix = <> option or choose a contrast!")} if (is.null(control)) { nc <- nrow(contrastMatrix) weights.help <- weightMatrix(n, contrast.matrix = contrastMatrix) weight<-weights.help$weight.matrix weight.help <- weights.help$weight.help cmpid <- paste("C", 1:nc) type.of.contrast <- "User Defined" } else { stop("Please declare the control group via your contrast matrix!") } type.of.contrast <- "User Defined" }, Tukey = { if (is.null(control)) { if (alternative!="two.sided"){stop("The Tukey contrast can only be tested two-sided!")} nc <- a * (a - 1)/2 cmpid <- sapply(1:nc, function(arg) { i <- vi[arg] j <- vj[arg] paste("p", "(", fl[i], ",", fl[j], ")", sep = "") }) weights.help <- weightMatrix(n, "Tukey") weight<-weights.help$weight.matrix weight.help <- weights.help$weight.help } else { stop("The Tukey contrast hasn't got a control group!") } type.of.contrast <- "Tukey" }, Dunnett = { nc <- a - 1 if (is.null(control)) { cont <- 1 } else { if (!any(fl == control)) { stop("The dataset doesn't contain this control group!") } cont <- which(fl == control) } vj <- which((1:a) != cont) vi <- rep(cont, a - 1) weights.help <- weightMatrix(n, "Dunnett",cont) weight<-weights.help$weight.matrix weight.help <- weights.help$weight.help cmpid <- sapply(1:nc, function(arg) { i <- vi[arg] j <- vj[arg] paste("p", "(", fl[i], ",", fl[j], ")", sep = "") }) type.of.contrast <- "Dunnett" }, Sequen = { if (is.null(control)) { nc <- a - 1 vi <- 1:(a - 1) vj <- 2:a weights.help <- weightMatrix(n, "Sequen") weight<-weights.help$weight.matrix weight.help <- weights.help$weight.help cmpid <- sapply(1:nc, function(arg) { i <- vj[arg] j <- vi[arg] paste("p", "(", fl[j], ",", fl[i], ")", sep = "") }) type.of.contrast <- "Sequen" } else { stop("The Sequen-Contrast hasn't got a control group!") } }, Williams = { if (is.null(control)) { nc <- a - 1 weights.help <- weightMatrix(n, "Williams") weight<-weights.help$weight.matrix weight.help <- weights.help$weight.help cmpid <- paste("C", 1:(a - 1)) type.of.contrast <- "Williams" } else { stop("The Williams contrast hasn't got a control group!") } }, Changepoint = { if (is.null(control)) { nc <- a - 1 weights.help <- weightMatrix(n, "Changepoint") weight<-weights.help$weight.matrix weight.help <- weights.help$weight.help cmpid <- paste("C", 1:(a - 1)) type.of.contrast <- "Changepoint" } else { stop("The Changepoint-Contrast hasn't got a control group!") } }, AVE = { if (is.null(control)) { nc <- a weights.help <- weightMatrix(n, "AVE") weight<-weights.help$weight.matrix weight.help <- weights.help$weight.help cmpid <- paste("C", 1:a) type.of.contrast <- "Average" } else { stop("The Average-Contrast hasn't got a control group!") } }, McDermott = { if (is.null(control)) { nc <- a - 1 weights.help <- weightMatrix(n, "McDermott") weight<-weights.help$weight.matrix weight.help <- weights.help$weight.help cmpid <- paste("C", 1:(a - 1)) type.of.contrast <- "McDermott" } else { stop("The McDermott-Contrast hasn't got a control group!") } }, UmbrellaWilliams = { if (is.null(control)) { nc <- a * (a - 1)/2 weights.help <- weightMatrix(n, "UmbrellaWilliams") weight<-weights.help$weight.matrix weight.help <- weights.help$weight.help cmpid <- paste("C", 1:(a * (a - 1)/2)) type.of.contrast <- "Umbrella Williams" } else { stop("The Umbrella Williams-Contrast hasn't got a control group!") } }, Marcus = { if (is.null(control)) { nc <- a * (a - 1)/2 weights.help <- weightMatrix(n, "Marcus") weight<-weights.help$weight.matrix weight.help <- weights.help$weight.help cmpid <- paste("C", 1:(a * (a - 1)/2)) type.of.contrast <- "Marcus" } else { stop("The Marcus-Contrast hasn't got a control group!") } } ) pd1 <- (pd == 1) pd0 <- (pd == 0) pd[pd1] <- 0.999 pd[pd0] <- 0.001 pd.help1 <- c(pd, 1-pd) pd <- c(weight%*%pd.help1) cov.bf <- weight %*% cov.bf %*% t(weight) for (i in 1:nc) { if (cov.bf[i, i] == 0) { cov.bf[i, i] <- 0.001 } } vd.bf <- c(diag(cov.bf)) vd.bf <- c(vd.bf) rho.bf <- corr.mat(cov.bf, nc) t.bf <- sqrt(ntotal) * (pd - 1/2)/sqrt(vd.bf) rownames(weight) <- paste("C", 1:nc) ncomp <- a * (a - 1)/2 tmp <- expand.grid(1:a, 1:a) ind <- tmp[[1]] > tmp[[2]] v2 <- tmp[[2]][ind] v1 <- tmp[[1]][ind] namen1 <- sapply(1:ncomp, function(arg) { i <- v2[arg] j <- v1[arg] paste("p", "(", fl[i], ",", fl[j], ")", sep = "") }) namen2 <- sapply(1:ncomp, function(arg) { i <- v2[arg] j <- v1[arg] paste("p", "(", fl[j], ",", fl[i], ")", sep = "") }) colnames(weight) <- c(namen1,namen2) df.sw[is.nan(df.sw)] <- 1000 df.sw <- weight %*% c(df.sw,df.sw) df.sw <- max(4, min(df.sw)) pd <- c(pd) logit.pd <- logit(pd) logit.dev <- diag(1/(pd * (1 - pd))) logit.cov <- logit.dev %*% cov.bf %*% t(logit.dev) vd.logit <- c(diag(logit.cov)) t.logit <- (logit.pd) * sqrt(ntotal/vd.logit) probit.pd <- qnorm(pd) probit.dev <- diag(sqrt(2 * pi)/(exp(-0.5 * qnorm(pd) * qnorm(pd)))) probit.cov <- probit.dev %*% cov.bf %*% t(probit.dev) vd.probit <- c(diag(probit.cov)) t.probit <- (probit.pd) * sqrt(ntotal/vd.probit) p.bfn = p.bft = p.bflogit = p.bfprobit = c() p.n = p.t = p.logit = p.probit = c() if (alternative == "two.sided") { z.bft <- z.quantile(conflevel = conflevel, corr = rho.bf, "two.sided", df = df.sw, dbs = "t") z.bfn <- z.quantile(conflevel = conflevel, corr = rho.bf, "two.sided", df = 0, dbs = "n") lower.bft <- pd - sqrt(vd.bf/ntotal) * z.bft upper.bft <- pd + sqrt(vd.bf/ntotal) * z.bft lower.bfn <- pd - sqrt(vd.bf/ntotal) * z.bfn upper.bfn <- pd + sqrt(vd.bf/ntotal) * z.bfn lower.logit <- expit(logit.pd - sqrt(vd.logit/ntotal) * z.bfn) upper.logit <- expit(logit.pd + sqrt(vd.logit/ntotal) * z.bfn) lower.probit <- pnorm(probit.pd - sqrt(vd.probit/ntotal) * z.bfn) upper.probit <- pnorm(probit.pd + sqrt(vd.probit/ntotal) * z.bfn) #---------------------------------------------------------# #--------------Berechnung von p-Werten -------------------# for (i in 1:nc) { p.bft[i] <- 1-pmvt(lower=-abs(t.bf[i]), abs(t.bf[i]), df=df.sw, corr=rho.bf,delta=rep(0,nc)) p.bfn[i] <- 1-pmvnorm(lower=-abs(t.bf[i]), abs(t.bf[i]),corr=rho.bf,mean=rep(0,nc)) p.bflogit[i] <- 1-pmvnorm(lower=-abs(t.logit[i]), abs(t.logit[i]),corr=rho.bf ,mean=rep(0,nc)) p.bfprobit[i] <- 1-pmvnorm(lower=-abs(t.probit[i]), abs(t.probit[i]),corr=rho.bf,mean=rep(0,nc)) p.tt <- pt(t.bf[i], df.sw) p.t[i] <- min(2*p.tt,2-2*p.tt) p.nn <- pnorm(t.bf[i]) p.n[i] <- min(2*p.nn, 2-2*p.nn) p.ll <- pnorm(t.logit[i]) p.logit[i] <- min(2*p.ll, 2-2*p.ll) p.pp <- pnorm(t.probit[i]) p.probit[i] <- min(2*p.pp, 2-2*p.pp) } text.output.p <- "H_0: p(i,j)=1/2" text.output.KI <- paste(100 * conflevel, "%", "2-sided", "Simultaneous-Confidence-Intervals for Relative Effects") upper <- "]" lower <- "[" } if (alternative == "greater") { z.bft <- z.quantile(conflevel = conflevel, corr = rho.bf, "less", df = df.sw, dbs = "t") z.bfn <- qmvnorm(conflevel, corr = rho.bf, tail = "lower")$quantile lower.bft <- pd - sqrt(vd.bf/ntotal) * z.bft lower.bfn <- pd - sqrt(vd.bf/ntotal) * z.bfn lower.logit <- expit(logit.pd - sqrt(vd.logit/ntotal) * z.bfn) lower.probit <- pnorm(probit.pd - sqrt(vd.probit/ntotal) * z.bfn) upper.bft = upper.probit = upper.logit = upper.bfn = 1 for (i in 1:nc) { p.bfn[i] <- 1 - pmvnorm(lower = -Inf, upper = t.bf[i], mean = rep(0, nc), corr = rho.bf) p.bft[i] <- 1 - pmvt(lower = -Inf, upper = t.bf[i], delta = rep(0, nc), df = df.sw, corr = rho.bf) p.bflogit[i] <- 1 - pmvnorm(lower = -Inf, upper = t.logit[i], mean = rep(0, nc), corr = rho.bf) p.bfprobit[i] <- 1 - pmvnorm(lower = -Inf, upper = t.probit[i], mean = rep(0, nc), corr = rho.bf) p.t[i] <-1-pt(t.bf[i], df.sw) p.n[i] <- 1-pnorm(t.bf[i]) p.logit[i] <- 1- pnorm(t.logit[i]) p.probit[i] <-1- pnorm(t.probit[i]) } text.output.p <- "H_0: p(i,j)<=1/2" text.output.KI <- paste(100 * conflevel, "%", "1-sided", "Simultaneous-Confidence-Intervals for Relative Effects") upper <- "]" lower <- "(" } if (alternative == "less") { z.bft <- z.quantile(conflevel = conflevel, corr = rho.bf, "less", df = df.sw, dbs = "t") z.bfn <- z.quantile(conflevel = conflevel, corr = rho.bf, "less", df = 0, dbs = "n") upper.bft <- pd + sqrt(vd.bf/ntotal) * z.bft upper.bfn <- pd + sqrt(vd.bf/ntotal) * z.bfn upper.logit <- expit(logit.pd + sqrt(vd.logit/ntotal) * z.bfn) upper.probit <- pnorm(probit.pd + sqrt(vd.probit/ntotal) * z.bfn) lower.bft = lower.probit = lower.logit = lower.bfn = 0 for (i in 1:nc) { p.bfn[i] <- 1 - pmvnorm(lower = t.bf[i], upper = Inf, mean = rep(0, nc), corr = rho.bf) p.bft[i] <- 1 - pmvt(lower = t.bf[i], upper = Inf, delta = rep(0, nc), df = df.sw, corr = rho.bf) p.bflogit[i] <- 1 - pmvnorm(lower = t.logit[i], upper = Inf, mean = rep(0, nc), corr = rho.bf) p.bfprobit[i] <- 1 - pmvnorm(lower = t.probit[i], upper = Inf, mean = rep(0, nc), corr = rho.bf) p.t[i] <- pt(t.bf[i], df.sw) p.n[i] <- pnorm(t.bf[i]) p.logit[i] <- pnorm(t.logit[i]) p.probit[i] <- pnorm(t.probit[i]) } text.output.p <- " H_0: p(i,j)>=1/2" text.output.KI <- paste(100 * conflevel, "%", "1-sided", "Simultaneous-Confidence-Intervals for Relative Effects") upper <- ")" lower <- "[" } bfn.lower <- round(lower.bfn, rounds) bfn.upper <- round(upper.bfn, rounds) bft.lower <- round(lower.bft, rounds) bft.upper <- round(upper.bft,rounds) logit.lower <- round(lower.logit, rounds) logit.upper <- round(upper.logit,rounds) probit.lower <- round(lower.probit, rounds) probit.upper <- round(upper.probit,rounds) p.bflogit <- round(p.bflogit, rounds) p.bfprobit <- round(p.bfprobit, rounds) p.bft <- round(p.bft, rounds) p.bfn <- round(p.bfn, rounds) p.logit <- round(p.logit, rounds) p.probit <- round(p.probit, rounds) p.t <- round(p.t, rounds) p.n <- round(p.n, rounds) pd <- round(pd, rounds) if (correlation == TRUE) { Correlation <- list(Correlation.matrix.N = rho.bf, Covariance.matrix.N = cov.bf, Warning = paste("Attention! The covariance matrix is multiplied with N", "=", ntotal)) } else { Correlation <- NA } data.info <- data.frame(row.names = 1:a, Sample = fl, Size = n) switch(asy.method, logit = { x.werte = cbind(lower.logit, pd, upper.logit) result <- list(weight.matrix = weight, Data.Info = data.info, Analysis.of.relative.effects = data.frame(row.names = c(1:nc), comparison = cmpid, rel.effect = pd, lower= logit.lower, upper=logit.upper, t.value = t.logit, p.adj = p.bflogit, p.raw= p.logit), Mult.Distribution = data.frame(Quantile = z.bfn, p.Value.global = min(p.bflogit)), Correlation = Correlation) Asymptotic.Method <- "Multivariate Delta-Method (Logit)" }, probit = { x.werte = cbind(lower.probit, pd, upper.probit) result <- list(weight.matrix = weight, Data.Info = data.info, Analysis.of.relative.effects = data.frame(row.names = c(1:nc), comparison = cmpid, rel.effect = pd, lower = probit.lower, upper = probit.upper, t.value = t.probit, p.adj = p.bfprobit, p.raw = p.probit), Mult.Distribution = data.frame(Quantile = z.bfn, p.Value.global = min(p.bfprobit)), Correlation = Correlation) Asymptotic.Method <- "Multivariate Delta-Method (Probit)" }, normal = { x.werte = cbind(lower.bfn, pd, upper.bfn) result <- list(weight.matrix = weight, Data.Info = data.info, Analysis.of.relative.effects = data.frame(row.names = c(1:nc), comparison = cmpid, rel.effect = pd, lower = bfn.lower, upper=bfn.upper, t.value = t.bf, p.adj = p.bfn, p.raw = p.n), Mult.Distribution = data.frame(Quantile = z.bfn, p.Value.global = min(p.bfn)), Correlation = Correlation) Asymptotic.Method <- "Multivariate Normal Distribution" }, mult.t = { x.werte = cbind(lower.bft, pd, upper.bft) result <- list(weight.matrix = weight, Data.Info = data.info, Analysis.of.relative.effects = data.frame(row.names = c(1:nc), comparison = cmpid, rel.effect = pd, lower = bft.lower, upper=bft.upper, t.value = t.bf, p.adj = p.bft,p.raw = p.t), Mult.Distribution = data.frame(Quantile = z.bft, p.Value.global = min(p.bft), d.f. = df.sw), Correlation = Correlation) Asymptotic.Method <- paste("Multi t - Distribution with d.f.= ", round(df.sw, 4)) }) if (plot.simci == TRUE) { test <- matrix(c(1:nc), ncol = nc, nrow = nc) angaben <- c(cmpid) angaben <- matrix(c(angaben), ncol = nc, nrow = nc) k <- c(1:nc) plot(x.werte[, 2], k, xlim = c(0, 1), axes = FALSE, type = "p", pch = 15, xlab = "", ylab = "") abline(v = 0.5, col = "red", lty = 1, lwd = 2) axis(1, at = seq(0, 1, 0.1)) axis(2, at = test, labels = angaben) axis(4, at = test, labels = test) points(x = x.werte[, 3], y = test[, 1], pch = upper) points(x = x.werte[, 1], y = test[, 1], pch = lower) for (i in 1:nc) { polygon(c(x.werte[i, 1], x.werte[i, 3]), c(i, i)) } box() title(main = c(text.output.KI, paste("Type of Contrast:", "", type.of.contrast, sep = ""), paste("Method:", "", Asymptotic.Method, sep = "")), ylab = "Comparison", xlab = paste("lower", lower, "-----", "p", "------", upper, "upper")) } if (info == TRUE) { cat("\n", "", "Nonparametric Multiple Comparison Procedure based on relative contrast effects", ",", "Type of Contrast", ":", type.of.contrast, "\n", "NOTE:", "\n", "*-------------------Weight Matrix------------------*", "\n", "-", "Weight matrix for choosen contrast based on all-pairs comparisons", "\n", "\n", "*-----------Analysis of relative effects-----------*", "\n", "-", "Simultaneous Confidence Intervals for relative effects p(i,j)\n with confidence level", conflevel, "\n", "-", "Method", "=", Asymptotic.Method, "\n", "-", "p-Values for ", text.output.p, "\n", "\n", "*----------------Interpretation--------------------*", "\n", "p(a,b)", ">", "1/2", ":", "b tends to be larger than a", "\n", "*--------------Mult.Distribution-------------------*", "\n", "-", "Equicoordinate Quantile", "\n", "-", "Global p-Value", "\n", "*--------------------------------------------------*", "\n") } return(result) } weightMatrix <- function(n,type = c("UserDefined","Tukey","AVE","Dunnett", "Sequen", "Changepoint", "Marcus", "McDermott", "Williams", "UmbrellaWilliams"), base = 1, contrast.matrix=NULL) { a <- length (n) n.col <- a*(a-1)/2 tmp <- expand.grid(1:a, 1:a) ind <- tmp[[1]] > tmp[[2]] vi <- tmp[[2]][ind] vj <- tmp[[1]][ind] type <- match.arg(type) switch (type, UserDefined = { if (is.null(contrast.matrix)){stop("Choose a contrast or give a contrast matrix by using 'contrast.matrix = matrix'")} #if(any(abs(contrast.matrix)>1)){stop("The contrast weights must be between 0 and 1!")} if (ncol(contrast.matrix)!=a){stop("The contrast matrix has more or less columns than samples!")} nc<-nrow(contrast.matrix) for (i in 1:nc){ places_pos<-(contrast.matrix[i,]>0) places_neg<-(contrast.matrix[i,]<0) sum_pos<-sum(contrast.matrix[i,][places_pos]) sum_neg<-sum(contrast.matrix[i,][places_neg]) if (abs(sum_neg)!= sum(sum_pos)) { stop(" Wrong contrast matrix!The sum of negative and positive weights must be equal") } for ( j in 1:a){ if (contrast.matrix[i,j]<0){ contrast.matrix[i,j]<-contrast.matrix[i,j]/abs(sum_neg)} if (contrast.matrix[i,j]>0){ contrast.matrix[i,j]<-contrast.matrix[i,j]/sum_pos} } } n.row <- nrow(contrast.matrix) ch <- contrast.matrix w<-matrix(0,nrow = n.row , ncol=n.col ) weight.help2<-matrix(0,nrow = n.row , ncol=n.col ) weight.help1<-matrix(0,nrow = n.row , ncol=n.col ) for (i in 1:n.row){ for (j in 1:n.col){ help <- c(rep(i,n.col)) a <- help[j] b <- vi[j] d <- vj[j] w[i,j] <- ch[a,b] *ch[a,d] if (w[i,j]>0){w[i,j]<-0} if (ch[a,b] > 0 && ch[a,d] < 0) {weight.help2[i,j]<-1} if(ch[a,b] < 0 && ch[a,d] > 0){weight.help1[i,j] <-1} } } w1<-w*weight.help1 w2<-w*weight.help2 w<--1*cbind(w1,w2) w }, AVE = { n.row <- a ch <- contrMat(n, type="AVE") w<-matrix(0,nrow = n.row , ncol=n.col ) weight.help2<-matrix(0,nrow = n.row , ncol=n.col ) weight.help1<-matrix(0,nrow = n.row , ncol=n.col ) for (i in 1:n.row){ for (j in 1:n.col){ help <- c(rep(i,n.col)) a <- help[j] b <- vi[j] d <- vj[j] w[i,j] <- ch[a,b] *ch[a,d] if (w[i,j]>0){w[i,j]<-0} if (ch[a,b] > 0 && ch[a,d] < 0) {weight.help2[i,j]<-1} if(ch[a,b] < 0 && ch[a,d] > 0){weight.help1[i,j] <-1} } } w1<-w*weight.help1 w2<-w*weight.help2 w<--1*cbind(w1,w2) w }, Changepoint = { n.row <- a - 1 ch <- contrMat(n, type = "Changepoint") w<-matrix(0,nrow = n.row , ncol=n.col ) weight.help2<-matrix(0,nrow = n.row , ncol=n.col ) weight.help1<-matrix(0,nrow = n.row , ncol=n.col ) for (i in 1:n.row){ for (j in 1:n.col){ help <- c(rep(i,n.col)) a <- help[j] b <- vi[j] d <- vj[j] w[i,j] <- ch[a,b] *ch[a,d] if (w[i,j]>0){w[i,j]<-0} if (ch[a,b] > 0 && ch[a,d] < 0) {weight.help2[i,j]<-1} if(ch[a,b] < 0 && ch[a,d] > 0){weight.help1[i,j] <-1} } } w1<-w*weight.help1 w2<-w*weight.help2 w<--1*cbind(w1,w2) w }, Dunnett = { n.row <- a - 1 ch <- contrMat(n, type = "Dunnett", base = base) w<-matrix(0,nrow = n.row , ncol=n.col ) weight.help2<-matrix(0,nrow = n.row , ncol=n.col ) weight.help1<-matrix(0,nrow = n.row , ncol=n.col ) for (i in 1:n.row){ for (j in 1:n.col){ help <- c(rep(i,n.col)) a <- help[j] b <- vi[j] d <- vj[j] w[i,j] <- ch[a,b] *ch[a,d] if (w[i,j]>0){w[i,j]<-0} if (ch[a,b] > 0 && ch[a,d] < 0) {weight.help2[i,j]<-1} if(ch[a,b] < 0 && ch[a,d] > 0){weight.help1[i,j] <-1} } } w1<-w*weight.help1 w2<-w*weight.help2 w<--1*cbind(w1,w2) w }, Sequen = { n.row <- a - 1 ch <- contrMat(n, type = "Sequen", base) w<-matrix(0,nrow = n.row , ncol=n.col ) weight.help2<-matrix(0,nrow = n.row , ncol=n.col ) weight.help1<-matrix(0,nrow = n.row , ncol=n.col ) for (i in 1:n.row){ for (j in 1:n.col){ help <- c(rep(i,n.col)) a <- help[j] b <- vi[j] d <- vj[j] w[i,j] <- ch[a,b] *ch[a,d] if (w[i,j]>0){w[i,j]<-0} if (ch[a,b] > 0 && ch[a,d] < 0) {weight.help2[i,j]<-1} if(ch[a,b] < 0 && ch[a,d] > 0){weight.help1[i,j] <-1} } } w1<-w*weight.help1 w2<-w*weight.help2 w<--1*cbind(w1,w2) w }, Marcus = { n.row <- a*(a-1)/2 ch <- contrMat(n, type = "Marcus", base) w<-matrix(0,nrow = n.row , ncol=n.col ) weight.help2<-matrix(0,nrow = n.row , ncol=n.col ) weight.help1<-matrix(0,nrow = n.row , ncol=n.col ) for (i in 1:n.row){ for (j in 1:n.col){ help <- c(rep(i,n.col)) a <- help[j] b <- vi[j] d <- vj[j] w[i,j] <- ch[a,b] *ch[a,d] if (w[i,j]>0){w[i,j]<-0} if (ch[a,b] > 0 && ch[a,d] < 0) {weight.help2[i,j]<-1} if(ch[a,b] < 0 && ch[a,d] > 0){weight.help1[i,j] <-1} } } w1<-w*weight.help1 w2<-w*weight.help2 w<--1*cbind(w1,w2) w }, McDermott = { n.row <- a -1 ch <- contrMat(n, type = "McDermott", base) w<-matrix(0,nrow = n.row , ncol=n.col ) weight.help2<-matrix(0,nrow = n.row , ncol=n.col ) weight.help1<-matrix(0,nrow = n.row , ncol=n.col ) for (i in 1:n.row){ for (j in 1:n.col){ help <- c(rep(i,n.col)) a <- help[j] b <- vi[j] d <- vj[j] w[i,j] <- ch[a,b] *ch[a,d] if (w[i,j]>0){w[i,j]<-0} if (ch[a,b] > 0 && ch[a,d] < 0) {weight.help2[i,j]<-1} if(ch[a,b] < 0 && ch[a,d] > 0){weight.help1[i,j] <-1} } } w1<-w*weight.help1 w2<-w*weight.help2 w<--1*cbind(w1,w2) w }, Williams = { n.row <- a - 1 ch <- contrMat(n, type = "Williams") w<-matrix(0,nrow = n.row , ncol=n.col ) weight.help2<-matrix(0,nrow = n.row , ncol=n.col ) weight.help1<-matrix(0,nrow = n.row , ncol=n.col ) for (i in 1:n.row){ for (j in 1:n.col){ help <- c(rep(i,n.col)) a <- help[j] b <- vi[j] d <- vj[j] w[i,j] <- ch[a,b] *ch[a,d] if (w[i,j]>0){w[i,j]<-0} if (ch[a,b] > 0 && ch[a,d] < 0) {weight.help2[i,j]<-1} if(ch[a,b] < 0 && ch[a,d] > 0){weight.help1[i,j] <-1} } } w1<-w*weight.help1 w2<-w*weight.help2 w<--1*cbind(w1,w2) w } , Tukey = { n.row <- a*(a-1)/2 ch <- contrMat(n, type = "Tukey", base) w<-matrix(0,nrow = n.row , ncol=n.col ) weight.help2<-matrix(0,nrow = n.row , ncol=n.col ) weight.help1<-matrix(0,nrow = n.row , ncol=n.col ) for (i in 1:n.row){ for (j in 1:n.col){ help <- c(rep(i,n.col)) a <- help[j] b <- vi[j] d <- vj[j] w[i,j] <- ch[a,b] *ch[a,d] if (w[i,j]>0){w[i,j]<-0} if (ch[a,b] > 0 && ch[a,d] < 0) {weight.help2[i,j]<-1} if(ch[a,b] < 0 && ch[a,d] > 0){weight.help1[i,j] <-1} } } w1<-w*weight.help1 w2<-w*weight.help2 w<--1*cbind(w1,w2) w }, UmbrellaWilliams = { n.row <- a*(a-1)/2 ch <- contrMat(n, type = "UmbrellaWilliams", base) w<-matrix(0,nrow = n.row , ncol=n.col ) weight.help2<-matrix(0,nrow = n.row , ncol=n.col ) weight.help1<-matrix(0,nrow = n.row , ncol=n.col ) for (i in 1:n.row){ for (j in 1:n.col){ help <- c(rep(i,n.col)) a <- help[j] b <- vi[j] d <- vj[j] w[i,j] <- ch[a,b] *ch[a,d] if (w[i,j]>0){w[i,j]<-0} if (ch[a,b] > 0 && ch[a,d] < 0) {weight.help2[i,j]<-1} if(ch[a,b] < 0 && ch[a,d] > 0){weight.help1[i,j] <-1} } } w1<-w*weight.help1 w2<-w*weight.help2 w<--1*cbind(w1,w2) w } ) ch.out <- matrix(c(ch),nrow=n.row) result <- list(weight.matrix = w, weight.help1 = weight.help1,weight.help2 = weight.help2,contrast.matrix = ch.out) result } nparcomp.wrapper <- function(model, data, hypotheses, alpha, alternative, asy.method) { control <- NULL type <- NULL contrastMatrix <- NULL if (hypotheses %in% c("Tukey", "Dunnett", "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus", "UmbrellaWilliams")) { type <- hypotheses } else { type <- "UserDefined" contrastMatrix <- hypotheses } result <- nparcomp(formula=formula(model), data, type = type, control = control, conflevel = 1-alpha, alternative, rounds = 3, correlation = TRUE, asy.method, plot.simci = FALSE, info = TRUE, contrastMatrix = contrastMatrix) pvalues <- result$Analysis.of.relative.effects$p.adj rejected1 <- (pvalues < alpha) confi <- cbind(result$Analysis.of.relative.effects$rel.effect, result$Analysis.of.relative.effects$lower, result$Analysis.of.relative.effects$upper) rownames(confi)<-result$Analysis.of.relative.effects$comparison temp <- cbind(confi,pvalues) colnames(temp) <- c("Estimate", "Lower","Upper","pValue") print(temp) return(list(adjPValues=pvalues, rejected=rejected1, confIntervals= confi, errorControl = new(Class='ErrorControl',type="FWER",alpha=alpha))) } mutoss.nparcomp <- function() { return(new(Class="MutossMethod", label="Nonparametric relative contrast effects", errorControl="FWER", callFunction="nparcomp.wrapper", output=c("adjPValues", "rejected","confIntervals", "errorControl"), info="

Nonparametric relative contrast effects

With this function, it is possible to compute nonparametric simultaneous confidence\ intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes\ adjusted p-values. The simultaneous confidence intervals can be computed using\ multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation\ of the degree of freedom or using multivariate range preserving transformations with Logit or\ Probit as transformation function. There is no assumption on the underlying distribution function, only\ that the data have to be at least ordinal numbers.

Reference:

", parameters=list( data=list(type="data.frame"), model=list(type="ANY"), hypotheses=list(type="ANY"), alpha=list(type="numeric"), alternative=list(type="character", label="Alternative", choices=c("two.sided", "less", "greater")), asy.method=list(type="character", label="Asymptotic approx. method", choices=c("logit", "probit", "normal", "mult.t")) ) )) } mutoss/R/pi0Est.R0000644000176200001440000001435415123457163013312 0ustar liggesusers# Collection of elementary functions calculating an estimate # of the number m0 resp. the proportion pi0 of true null hypotheses # in a finite family of hypotheses. # # Author: MarselScheer and WiebkeWerft ############################################################################### storey_pi0_est = function(pValues, lambda) { pi0 = (sum(pValues > lambda) + 1) / (1 - lambda) / length(pValues) return(list(pi0 = pi0, lambda = lambda)) } mutoss.storey_pi0_est <- function() { return(new(Class="MutossMethod", label="Storey-Taylor-Siegmund (2004) Procedure", callFunction="storey_pi0_est", output=c("pi0", "lambda"), info="

Storey-Taylor-Siegmund procedure

\n\n\

The Storey-Taylor-Siegmund procedure for estimating pi0 is applied to pValues.\ The formula is equivalent to that in Schweder and Spjotvoll (1982),\ page 497, except the additional '+1' in the nominator that\ introduces a conservative bias which is proven to be sufficiently large\ for FDR control in finite families of hypotheses if the estimation\ is used for adjusting the nominal level of a linear step-up test.

\n\

Reference:

\ ", parameters=list(pValues=list(type="numeric"), lambda=list(type="numeric")) )) } ABH_pi0_est <- function(pValues) { m <- length(pValues) index <- order(pValues) spval <- pValues[index] m0.m <- rep(0, m) for (k in 1:m) { m0.m[k] <- (m + 1 - k)/(1 - spval[k]) } idx <- which(diff(m0.m, na.rm = TRUE) > 0) if (length(idx) == 0) grab <- 2 else grab <- min(idx, na.rm = TRUE)+1 pi0.ABH <- (ceiling(min(m0.m[grab], m))/m) return(list(pi0 = pi0.ABH)) } mutoss.ABH_pi0_est <- function() { return(new(Class="MutossMethod", label="Hochberg-Benjamini (1990) lowest slope line method", callFunction="ABH_pi0_est", output=c("pi0"), info="

Hochberg-Benjamini lowest slope line method

\n\n\

The Lowest Slope Line (LSL) method of Hochberg and Benjamini for estimating pi0 is applied to pValues.\ This method for estimating pi0 is motivated by the graphical approach proposed\ by Schweder and Spjotvoll (1982), as developed and presented in Hochberg and Benjamini (1990).

\n\

Reference:

\ ", parameters=list(pValues=list(type="numeric")) )) } TSBKY_pi0_est <- function(pValues, alpha) { m <- length(pValues) adjp <- p.adjust(pValues,"BH") pi0.TSBKY <- ((m - sum(adjp < alpha/(1 + alpha), na.rm = TRUE)) / m) return(list(pi0=pi0.TSBKY)) } mutoss.TSBKY_pi0_est <- function() { return(new(Class="MutossMethod", label="Benjamini, Krieger and Yekutieli (2006) two-step estimation method", callFunction="TSBKY_pi0_est", output=c("pi0"), info="

Hochberg-Benjamini lowest slope line method

\n\n\

The two-step estimation method of Benjamini, Krieger and Yekutieli for estimating pi0 is applied to pValues. It consists of the following two steps:

\

Step 1. Use the linear step-up procedure at level alpha' =alpha/(1+alpha). Let r1 be the number of\ rejected hypotheses. If r1=0 do not reject any hypothesis and stop; if r1=m reject all m\ hypotheses and stop; otherwise continue.

\n\

Reference:

\ ", parameters=list(pValues=list(type="numeric"),alpha=list(type="numeric")) )) } # TODO: GB (MS) This will help will look terrible! BR_pi0_est <- function(pValues, alpha, lambda=1, truncate = TRUE) { if ( lambda <= 0 || lambda >= 1/alpha) { stop('BR_pi0_est() : lambda should belong to (0, 1/alpha)') } m <- length(pValues) stage1 <- indepBR( pValues, alpha, lambda, silent = TRUE) pi0 <- ( m + 1 - sum(stage1$rejected) ) / ( m * ( 1 - lambda*alpha ) ) if (truncate) { pi0 = min(1,pi0) } return(list(pi0=pi0)) } mutoss.BR_pi0_est <- function() { return(new(Class="MutossMethod", label="Blanchard-Roquain (2009) estimation method", callFunction="BR_pi0_est", output=c("pi0"), info="

Blanchard-Roquain estimation under independence

\n\n\

Reference:

\ \

The proportion of true\ nulls is estimated using the Blanchard-Roquain 1-stage procedure with parameter lambda,\ via the formula\n\ estimated pi0 = ( m - R(alpha,lambda) + 1) / ( m*( 1 - lambda * alpha ) )\n\ where R(alpha,lambda) is the number of hypotheses rejected by the BR 1-stage procedure, alpha is FDR level control for this procedure and lambda a parameter belonging to (0, 1/alpha) with default value 1. Independence of p-values is assumed. This estimate may in some cases be larger than 1; it is truncated to 1 if the parameter truncated=TRUE. The estimate is used in the Blanchard-Roquain 2-stage step-up (with the non-truncated version)

", parameters=list(pValues=list(type="numeric"),alpha=list(type="numeric"), lambda=list(type="numeric", default=1), truncate=list(type="logical", default=TRUE)) )) } mutoss/R/onLoad.R0000755000176200001440000000103615123457163013356 0ustar liggesusers.onLoad <- function(libname, pkgname) { # if(!require("multtest", character.only=TRUE)) { # if (interactive()) { # answer <- readline("Multtest is missing - do you want to install it (y/N)? ") # if (substr(answer, 1, 1) %in% c("y","Y")) { # source("http://bioconductor.org/biocLite.R") # biocLite("multtest") # require("multtest") # } else { # warning("Package multtest is not avaible - please install it!") # } # } else { # warning("Package multtest is not avaible - please install it!") # } # } } mutoss/R/SUDProcedures.R0000644000176200001440000011516615123457163014640 0ustar liggesusers# Here all implemented concrete SUD-Procedures can be found. # # Author: MarselScheer and WerftWiebke ############################################################################### #++++++++++++++++++++++++++++ OutputFkt +++++++++++++++++++++ printRejected = function(rejected, pValues = NULL, adjPValues = NULL) { cat("Number of hyp.:\t", length(rejected), "\n") cat("Number of rej.:\t", sum(rejected), "\n") idx <- which(rejected) if (length(idx) != 0) { output <- data.frame(rejected = idx) if (!is.null(pValues)) { output <- data.frame(output, pValues[idx]) names(output)[length(names(output))] <- "pValues" } if (!is.null(adjPValues)) { output <- data.frame(output, adjPValues[idx]) names(output)[length(names(output))] <- "adjPValues" } if (!is.null(pValues)) # sorting by pValues output <- output[order(output$pValue), ] else { if (!is.null(adjPValues)) # no pValues availible, sorting by adjPValues output <- output[order(output$adjPValues), ] } rownames(output) <- 1:length(idx) print(output) } } #---------------------------- OutputFkt --------------------- #Rom_simpleImplementation <- function(pValues, alpha) #{ # # ROM, D. M. (1990). A sequentially rejective test procedure based # # on a modified Bonferroni inequality. Biometrika 77, 663-665. # # # Formula for the critical values is taken from # # FINNER, H. and ROTERS, M. (2002). Multiple hypotheses testing and # # expected type I errors. Ann. Statist. 30, 220-238. # # Notice: The smallest critical value in this paper is alpha_1!! # # Thus the critical values are calculated in this manner, and # # at the end the order is reversed. # # # ++++++ Calculating critical values # # # TODO: Perhaps there are computational problems if too many hypotheses are tested. # len <- length(pValues) # criticalValues <- rep(0, times=len) # criticalValues[1] <- alpha # # # TODO: !! firstSum_k[14:len] is constant for example if alpha=5%, len=200 # # firstSum_k := SUMME(alpha^i, i=1..(k-1)) # firstSum_k <- cumsum(c(0, sapply(1:(len-1), function(i) alpha^i))) # # criticalValues[2] <- 1/2 * (firstSum_k[2] - 0) # secondSummand <- function(i) choose(k,i) * criticalValues[i+1]^(k-i) # for (k in 3:len) # { # # TODO: secondSum can be calculated faster! # secondSum <- sum(sapply(1:(k-2), secondSummand)) # criticalValues[k] <- 1 / k * (firstSum_k[k] - secondSum) # #cat("1 ", secondSum, "\n") # } # criticalValues <- criticalValues[len:1] # # # ------ Calculating critical Values # # SU(pValues, criticalValues) # #} # TODO: MS !! Discussion about big n !! rom <- function(pValues, alpha, silent = FALSE) { # # Remark: The critical values calculated by this procedure were # compared with the critical values calculated by Rom # himself in his paper and they are the same. # Formula for the critical values is taken from # FINNER, H. and ROTERS, M. (2002). Multiple hypotheses testing and # expected type I errors. Ann. Statist. 30, 220-238. # Notice: The smallest critical value in this paper is alpha_1!! # Thus the critical values are calculated in this manner, and # at the end the order is reversed. # ++++++ Calculating critical Values # TODO: MS perhaps there are computational problems if too many hypotheses are tested. len <- length(pValues) criticalValues <- rep(0, times=len) criticalValues[1] <- alpha # TODO: MS !! firstSum_k[14:len] is constant for example if alpha=5%, len=200 # firstSum_k := SUMME(alpha^i, i=1..(k-1)) firstSum_k <- cumsum(c(0, sapply(1:(len-1), function(i) alpha^i))) criticalValues[2] <- 1/2 * (firstSum_k[2] - 0) # SUMME(binomial(k,i) * alpha_{i+1}^{k-i}, i=1..(k-2)) # = SUMME(aki, i=1..(k-2)) # = SUMME(binomial(k-1, i) * k / (k-i) * a(k-1)i * alpha_{i+1}, i=1..(k-2)) # for k = 3 and i = 1 # aki and binKoef actually has 2 dimensions, the k-dimension and the i-dimension. # But in this code we will only work with the i-dimension. In every step of # the for-loop aki[i] will be updated. aki <- rep(0, times = (len-2)) binKoef <- rep(1, times = (len-2)) binKoef[1] <- choose(3,1) aki[1] <- criticalValues[2]^(3-1) for (k in 3:len) { secondSummand <- sum(binKoef[1:(k-2)] * aki[1:(k-2)]) criticalValues[k] <- 1/k * (firstSum_k[k] - secondSummand) # updating the vectors for the next step binKoef[k-1] <- (k+1) * k / 2 #choose(k+1, k-1) binKoef[1:(k-2)] <- binKoef[1:(k-2)] * (k+1) / (k:3) aki[k-1] <- criticalValues[k]^2 aki[1:(k-2)] <- aki[1:(k-2)] * criticalValues[2:(k-1)] } criticalValues <- criticalValues[len:1] # ------ Calculating critical Values rejected <- SU(pValues, criticalValues) if (! silent) { cat("\n\n\t\tRom's (1990) step-up procedure\n\n") printRejected(rejected, pValues, NULL) } # TODO: MS calculating adjustedPValues for ROM numerically return(list(rejected = rejected, criticalValues = criticalValues, errorControl = new(Class='ErrorControl',type="FWER",alpha=alpha)) ) } mutoss.rom <- function() { return(new(Class="MutossMethod", label="Rom's (1990) step-up", errorControl="FWER", callFunction="rom", output=c("rejected", "criticalValues","errorControl"), info="

Rom's step-up procedure

\n\n\

Rom's step-up-procedure is applied to pValues. The procedure controls the FWER in the strong sense if the pValues are stochastically independent.

This function calculates the critical values by the formula given in Finner, H. and Roters, M. (2002) based on the joint distribution of order statistics. After that a step-up test is performed to reject hypotheses associated with pValues.

Since the formula for the critical values is recursive, the calculation of adjusted pValues is far from obvious and is not implemented here.

Reference:

\ ", parameters=list(pValues=list(type="numeric"), alpha=list(type="numeric")) )) } #-------------------- Holm's Step-down--------------------# holm <- function(pValues, alpha, silent = FALSE) { m <- length(pValues) criticalValues <- sapply(1:m, function(i) alpha/(m-i+1)) adjPValues <- p.adjust(pValues, "holm") rejected <- (adjPValues <= alpha) if (! silent) { cat("\n\n\t\tHolm's (1979) step-down Procedure\n\n") printRejected(rejected, pValues, adjPValues) } return(list(adjPValues = adjPValues, rejected = rejected, criticalValues=criticalValues, errorControl = new(Class='ErrorControl',type="FWER",alpha=alpha)) ) } mutoss.holm <- function() { return(new(Class="MutossMethod", label="Holm's (1979) step-down", errorControl="FWER", callFunction="holm", output=c("adjPValues", "rejected", "criticalValues","errorControl"), info="

Holm's step-down-procedure

\n\n\

Holm's step-down-procedure is applied to pValues. It controls the FWER in the strong sense under arbitrary dependency.

\n\

Holm's procedure uses the same critical values as the Hochberg's procedure, namely c(i)=α/(m-i+1), but is a step-down version while Hochberg's method is a step-up version of the Bonferroni test. Holm's method is based on the Bonferroni inequality and is valid regardless of the joint distribution of the test statistics, whereas Hochberg's method relies on the assumption that Simes' inequality holds for the joint null distribution of the test statistics. If this assumption is met, Hochberg's step-up procedure is more powerful than Holm's step-down procedure.

\n\

Reference:

\ ", parameters=list(pValues=list(type="numeric"), alpha=list(type="numeric")) )) } # TODO: MS Probably jointCDF.unif should probably be moved to some math.R or so. # TODO: MS !! jointCDF.unif: There are numerical issues because the of accuracy of doublePrecison # TODO: MS !! How to communicate numerical issues to the user. jointCDF.orderedUnif = function(vec) { # vec is not ordered. Thus the probability must be 0 if (!all(order(vec) == 1:length(vec))) { print("ORDER!") print(vec) print(order(vec)) vecName <- deparse(substitute(vec)) warning(paste("jointCDF.unif(): The variable", vecName, "is not ordered. Thus the probability is 0!")) return(0) } if (min(vec) <= 0) return(0) vec[ vec > 1 ] <- 1 if (100 < length(vec)) warning("Length of the argument is longer than 100. Calculated value may not be useable!") # By Bolshev's recursion # P(U_{1:n} <= vec[1], ..., U_{n:n} <= vec[n]) # = Fn(vec[1], ..., vec[n]) # = 1 - sum( binom(n, j) * Fj(vec[1], ..., vec[j]) * (1-vec[j+1])^(n-j), j=0..n-1) # with F0 = 1. # The variable Fj[k+1] used in this function will correspond to # Fk(vec[1], ..., vec[k]) for all k = 0 .. n. # So Fj[1] is F0 = 1, Fj[2] is F1(vec[1]) and so on. Fj <- rep(0, times = length(vec) + 1) Fj[1] <- 1 # F0 # consider k; # Fj[k+1]; # Fk(vec[1], ..., vec[k]) # = 1 - sum( binom(k, s) * Fs(vec[1], ..., vec[s]) * (1-vec[s+1])^(n-s) , s=0..k-1 ) # = 1 - sum( choose(k,s) * Fj[s+1] * (1 - vec[s+1])^(k-s), s=0..k-1 ) summand <- function(s) choose(k,s) * Fj[s+1] * (1 - vec[s+1])^(k-s) for(k in 1:length(vec)) Fj[k+1] <- 1 - sum( sapply(0:(k-1), summand)) return(Fj[length(vec)+1]) } calculateBetaAdjustment = function(n, startIDX_SUD, alpha, silent = FALSE, initialBeta = 1, maxBinarySteps = 50, tolerance = 0.0001) { #+++++++++++++++++++++++++++ Subfunctions +++++++++++++++++++++++++ #probability mass function pmf <- function(criticalValues, startIDX_SUD, n, n0, j) {# Calculates P_{n,n0}(V = j) for a set of critical Values. # Formulas are from Finner, Gontscharuk, Dickhaus: FDR controlling step-up-down tests # related to the asmptotically optimal rejection curve. (to appear) if (n0 < j) return(0) n1 <- n - n0 if (startIDX_SUD <= n1) { if (j == 0) return( choose(n0,j) * 1 * (1-criticalValues[n1 + j + 1])^(n0-j) ) return( choose(n0,j) * jointCDF.orderedUnif(criticalValues[(n1 + 1):(n1 + j)]) * (1-criticalValues[n1 + j + 1])^(n0-j) ) } if ((n1 < startIDX_SUD) && (j < startIDX_SUD - n1 - 1)) { if (n1 + j == 0) # <=> n1 == 0 and j == 0 return(choose(n0,j) * jointCDF.orderedUnif(c(rep(1 - criticalValues[startIDX_SUD], times = (n - startIDX_SUD + 1)), 1 - criticalValues[(startIDX_SUD - 1):(n1 + j + 1)])) * 1 ) return(choose(n0,j) * jointCDF.orderedUnif(c(rep(1 - criticalValues[startIDX_SUD], times = (n - startIDX_SUD + 1)), 1 - criticalValues[(startIDX_SUD - 1):(n1 + j + 1)])) * criticalValues[n1 + j]^j ) } if ((n1 < startIDX_SUD) && (j == startIDX_SUD - n1 - 1)) { if (n1 + j == 0) # <=> n1 == 0 and j == 0 return(choose(n0,j) * jointCDF.orderedUnif(rep(1 - criticalValues[startIDX_SUD], times = (n - startIDX_SUD + 1))) * 1 ) return(choose(n0,j) * jointCDF.orderedUnif(rep(1 - criticalValues[startIDX_SUD], times = (n - startIDX_SUD + 1))) * criticalValues[n1 + j]^j ) } if ((n1 < startIDX_SUD) && (j == startIDX_SUD - n1)) return(choose(n0,j) * jointCDF.orderedUnif(rep(criticalValues[startIDX_SUD], times=j)) * (1 - criticalValues[n1 + j + 1])^(n0 - j) ) if ((n1 < startIDX_SUD) && (startIDX_SUD - n1 < j)) return(choose(n0,j) * jointCDF.orderedUnif(c(rep(criticalValues[startIDX_SUD], times=(startIDX_SUD - n1)), criticalValues[(startIDX_SUD + 1):(n1 + j)])) * (1 - criticalValues[n1 + j + 1])^(n0 - j) ) } calculateMaximumUpperFDRBound <- function(criticalValues, n, startIDX_SUD) { # Formulas are from Finner, Gontscharuk, Dickhaus: FDR controlling step-up-down tests # related to the asmptotically optimal rejection curve. (to appear) # Calculating the probability mass function of V under a DU(n_0, n) model # n = Number of hypotheses # n0 = Number of true hypotheses # pm[j+1,n0+1] = P_{n,n0}(V = j) pm <- sapply( 1:n, function(n0) { sapply(0:n, # actually we only had to go to n0, but in this way pm will be a matrix function(j) { pmf(criticalValues, startIDX_SUD, n, n0, j) } ) } ) # now the special case n0 = 0, then P_{n,n0}(V=0) = 1 pm <- cbind(c(1, rep(0, times = n)), pm) # just for plausibility, gonna look if P_{n,n0}(V in {0, ..., n}) = 1 for every n0 = 1, ..., n rng <- range(colSums(pm)) if (rng[2]-rng[1] > 0.01) warning("Maximum upper bounds of FDR probably not accurate!") # Calculating b(n,n0|startIDX_SUD) for every n0 # which is a uppper bound for the FDR according to Finner, Gontscharuk, Dickhaus. bn <- sapply(1:n, function(n0) { n1 <- n - n0 n0 * sum(sapply(1:n0, function(j) criticalValues[n1 + j]/(n1 + j) * pm[j, n0])) } ) bn return(max(bn)) } SearchInitialBetaInterval <- function(n, startIDX_SUD, alpha, initialBeta) { # searches two beta's: beta1 and beta2 such that the beta2-adjusted AORC # controls the FDR and the beta1-adjusted AORC not and beta2 + step = beta1 criticalValues <- sapply(1:n, function(i) i * alpha / (n + initialBeta - i * (1 - alpha))) UpperFDRBound <- calculateMaximumUpperFDRBound(criticalValues, n, startIDX_SUD) # startBeta controls the FDR, thus beta must be reduced! if (UpperFDRBound < alpha) { FDRControlOfInitialBeta <- TRUE step <- -1 } else { FDRControlOfInitialBeta <- FALSE step <- 1 } beta <- initialBeta + step intervalFound <- FALSE while (!intervalFound) { criticalValues <- sapply(1:n, function(i) i * alpha / (n + beta - i * (1 - alpha))) UpperFDRBound <- calculateMaximumUpperFDRBound(criticalValues, n, startIDX_SUD) # if we have FDRControl by the initialBeta but not for beta, then we are done! # Also if we have not control of the FDR by the initalBeta but for beta, then we are done! if (xor(UpperFDRBound < alpha, FDRControlOfInitialBeta)) intervalFound <- TRUE else # initialBeta and beta both control the FDR or both do not control the FDR beta <- beta + step } if (FDRControlOfInitialBeta) return(c(beta, beta + 1)) return(c(beta - 1, beta)) } #--------------------------- Subfunctions ------------------------- if (!silent) cat("Searching initial interval to start the bisection approach.\n") betaInt <- SearchInitialBetaInterval(n, startIDX_SUD, alpha, initialBeta) beta <- betaInt[2] step <- (betaInt[2] - betaInt[1]) / 2 numberOfSteps <- 0 if (!silent) cat("Starting the bisection approach.\n") lastFeasibleBeta <- beta lastFeasibleUpperFDRBound <- -Inf while(numberOfSteps < maxBinarySteps) { numberOfSteps <- numberOfSteps + 1 criticalValues <- sapply(1:n, function(i) i * alpha / (n + beta - i * (1 - alpha))) UpperFDRBound <- calculateMaximumUpperFDRBound(criticalValues, n, startIDX_SUD) if (!silent) cat("Step ", numberOfSteps, ": beta =", beta, " => Upper FDR bound =", UpperFDRBound, "\n") if (alpha - tolerance <= UpperFDRBound && UpperFDRBound <= alpha) { if (!silent) { cat("\nUpper FDR bound element in [alpha - tolerance, alpha]\n") cat("Returned beta =", beta, " => Upper FDR bound =", UpperFDRBound, "\n") } return(beta) } if (UpperFDRBound > alpha) beta <- beta + step else { lastFeasibleBeta <- min(beta, lastFeasibleBeta) lastFeasibleUpperFDRBound <- max(UpperFDRBound, lastFeasibleUpperFDRBound) beta <- beta - step } step <- step / 2 } if(!silent) cat("\nReturned beta =", lastFeasibleBeta, " => Upper FDR bound =", lastFeasibleUpperFDRBound, "\n" ) return(lastFeasibleBeta) } #------------------------ AORC---------------------# aorc <- function(pValues, alpha, startIDX_SUD = length(pValues), betaAdjustment, silent = FALSE) { len <- length(pValues) if (missing(betaAdjustment)) { if (!silent) cat("Using calculateBetaAdjustment() to set the missing parameter betaAdjustment.\n") betaAdjustment = calculateBetaAdjustment(len, startIDX_SUD, alpha, silent) } criticalValues <- sapply(1:len, function(i) i * alpha / (len + betaAdjustment - i * (1 - alpha))) rejected <- SUD(pValues, criticalValues, startIDX_SUD) if (! silent) { cat("\n\n\t\tAsymptotically optimal rejection curve (2009)\n\n") printRejected(rejected, pValues) } return(list(rejected = rejected, criticalValues = criticalValues, errorControl = new(Class='ErrorControl',type="FDR",alpha=alpha))) } mutoss.aorc <- function() { return(new(Class="MutossMethod", label="Asymptotically optimal rejection curve (2009)", errorControl="FDR", callFunction="aorc", output=c("criticalValues", "rejected", "errorControl"), info="

Step-up-down procedure based on the asymptotically optimal rejection curve

\n\n\

The graph of the function f(t) = t / (t * (1 - alpha) + alpha) is called the asymptotically \ optimal rejection curve. Denote by finv(t) the inverse of f(t). Using the \ critical values finv(i/n) for i = 1, ..., n yields asymptotic FDR control. \ To ensure finite FDR control it is possible to adjust f(t) by a factor. The \ function calculateBetaAdjustment() calculates a beta such that (1 + beta / n) * f(t) \ can be used to control the FDR for a given finite sample size. If beta is not provided, calculateBetaAdjustment() will be called automatically.\

\n\

Reference:

\ ", parameters=list(pValues=list(type="numeric"), alpha=list(type="numeric"), startIDX_SUD=list(type="integer", label="Start Index for Step-Up-Down", optional=TRUE), betaAdjustment=list(type="numeric", label="Adjustment factor beta_n", optional=TRUE)) )) } #aorc( runif(30), 0.05, 5) #----------------- Banjamini Liu----------------# BL <- function(pValues, alpha, silent=FALSE) { m <- length(pValues) criticalValues <- sapply(1:m, function(i) 1-(1-min(1, (m*alpha)/(m-i+1)))^(1/(m-i+1))) rejected <- SD(pValues, criticalValues) index <- order(pValues) # index for sorting pValues rindex <- order(index) # reversed index to obtain the original order spval <- pValues[index] adjPValues <- vector(mode="numeric",length=m) adjPValues[1] <- min(1 - (1 - spval[1])^m, 1) for (i in 2:m) adjPValues[i] <- max(adjPValues[i - 1], ifelse((alpha*m)/(m-i+1)<=1, ((m-i+1)/m)*(1 - (1 - spval[i])^(m - i + 1)), 0))#(0)?! adjPValues <- adjPValues[rindex] # obtain the original order #rejected <- (adjustedPValues <= alpha) # either this or SUD leads to rejected if (! silent) { cat("\n\n\t\tBenjamini-Liu's (1999) step-down procedure\n\n") printRejected(rejected, pValues, adjPValues) } return(list(adjPValues=adjPValues, criticalValues=criticalValues, rejected=rejected, errorControl = new(Class='ErrorControl',type="FDR",alpha=alpha))) } mutoss.BL <- function() { return(new(Class="MutossMethod", label="Benjamini-Liu (1999) step-down", errorControl="FDR", callFunction="BL", output=c("adjPValues", "criticalValues", "rejected", "errorControl"), assumptions=c("Independent test statistics."), info="

Benjamini-Liu (1999) step-down

\n\n

Reference:

Benjamini-Liu's step-down procedure is applied to pValues. The procedure controls the FDR if the corresponding test statistics are stochastically independent. In Benjamini and Liu (1999) a large simulation study concerning the power of the two procedures suggested that the BL step-down procedure is more powerfull then the Linear Step-Up (BH) when the number of hypotheses is small. This is also the case when most hypotheses are far from the null. The BL step-down method calculates critical values according to Benjamin and Liu (1999), i.e. ci = 1 - (1 - min(1, m*α/(m-i+1)))(1/(m-i+1)) for i = 1,...,m, where m is the number of hypotheses tested. Then, let k be the smallest i for which P(i) > ci and reject associated hypotheses H(1),...,H(k-1).", parameters=list(pValues=list(type="numeric"), alpha=list(type="numeric")) )) } #-------------------- BH Linear Step Up--------------------# BH <- function(pValues, alpha, silent=FALSE) { m <- length(pValues) criticalValues <- sapply(1:m, function(i) (i*alpha)/m) adjPValues <- p.adjust(pValues, "BH") rejected <- (adjPValues <= alpha) if (! silent) { cat("\n\n\t\tBenjamini-Hochberg's (1995) step-up procedure\n\n") printRejected(rejected, pValues, adjPValues) } return(list( adjPValues=adjPValues, criticalValues=criticalValues, rejected=rejected, errorControl = new(Class='ErrorControl',type="FDR",alpha=alpha))) } mutoss.BH <- function() { return(new(Class="MutossMethod", label="Benjamini-Hochberg (1995) step-up", errorControl="FDR", callFunction="BH", output=c("adjPValues", "criticalValues", "rejected", "errorControl"), assumptions=c("independence or positive regression dependency"), info="

Benjamini-Hochberg (1995) Linear Step-Up Procedure

\n\n\

The Benjamini-Hochberg (BH) linear step-up procedure controls the FDR if the test statistics are stochastically independent or satisfy positive regression dependency (see Benjamini and Yekutieli 2001 for details). In their seminal paper, Benjamini and Hochberg (1995) suggest the False Discovery Rate (FDR) as an alternative error criterion to the Family-Wise-Error-Rate and show that for 0<=m0<=m independent pValues corresponding to true null hypotheses and for any joint distribution of the m1=m-m0 p-values corresponding to the non-null hypotheses the FDR is controlled at level (m0/m)*α. Benjamini and Yekutieli show (2001) that this procedure controls the FDR in a much more general setting i.e. when the PRDS condition is satisfied. #####Benjamini et al. (2006) improved by adaptive procedures which use an estimate of m0 and apply the BH method at level α'=&alpha*m/m0, to fully exhaust the desired level α.

\n

References:

\ ", parameters=list(pValues=list(type="numeric"), alpha=list(type="numeric")) )) } BY <- function(pValues, alpha, silent=FALSE) { m <- length(pValues) a <- sum(1/(1:m)) criticalValues <- sapply(1:m, function(i) (i*alpha)/(a*m)) #rejected <- SU(pValues, criticalValues) adjPValues <- p.adjust(pValues, "BY") rejected <- (adjPValues <= alpha) if (! silent) { cat("\n\n\t\tBenjamini-Yekutieli's (2001) step-up procedure\n\n") printRejected(rejected, pValues, adjPValues) } return(list(adjPValues=adjPValues, criticalValues=criticalValues, rejected=rejected, errorControl = new(Class='ErrorControl',type="FDR",alpha=alpha))) } mutoss.BY <- function() { return(new(Class="MutossMethod", label="Benjamini-Yekutieli (2001) step-up", errorControl="FDR", callFunction="BY", output=c("adjPValues", "criticalValues", "rejected", "errorControl"), assumptions=c("any dependency structure"), info="

Benjamini-Yekutieli (2001) step-up procedure

\n\n\

Reference:

\

The Benjamini-Yekutieli step-up procedure is applied to pValues. The procedure ensures FDR control for any dependency structure. The critical values of the Benjamini-Yekutieli (BY) procedure are calculated by replacing the α of the Benjamini-Hochberg procedure by α/(∑1/i), i.e. c(i)=i*α/m*(∑1/i) for i=1,...,m. For large number m of hypotheses the critical values of the BY procedure and the BH procedure differ by a factor log(m). Benjamini and Yekutieli (2001) showed that this step-up procedure controls the FDR at level α*m/m0 for any test statistics dependency structure.

\n", parameters=list(pValues=list(type="numeric"), alpha=list(type="numeric")) )) } hochberg <- function(pValues, alpha, silent=FALSE) { m <- length(pValues) criticalValues <- sapply(1:m, function(i) alpha/(m-i+1)) #rejected <- SU(pValues, criticalValues) adjPValues <- p.adjust(pValues, "hochberg") rejected <- (adjPValues <= alpha) if (! silent) { cat("\n\n\t\tHochberg's (1988) step-up procedure\n\n") printRejected(rejected, pValues, adjPValues) } return(list(adjPValues=adjPValues, criticalValues=criticalValues, rejected=rejected, errorControl = new(Class='ErrorControl',type="FWER",alpha=alpha))) } mutoss.hochberg <- function() { return(new(Class="MutossMethod", label="Hochberg (1988) step-up", errorControl="FWER", callFunction="hochberg", output=c("adjPValues", "criticalValues", "rejected", "errorControl"), assumptions=c("independent tests"), info="

Hochberg (1988) step-up procedure

\n\n\

Reference:

\

The Hochberg step-up procedure is based on marginal p-values. It controls the FWER in the strong sense under joint null distributions of the test statistics that satisfy Simes' inequality. The Hochberg procedure is more powerful than Holm's (1979) procedure, but the test statistics need to be independent or have a distribution with multivariate total positivity of order two or a scale mixture thereof for its validity (Sarkar, 1998). Both procedures use the same set of critical values c(i)=α/(m-i+1). Whereas Holm's procedure is a step-down version of the Bonferroni test, and Hochberg's is a step-up version of the Bonferroni test. Note that Holm's method is based on the Bonferroni inequality and is valid regardless of the joint distribution of the test statistics.

\n", parameters=list(pValues=list(type="numeric"), alpha=list(type="numeric")) )) } adaptiveBH <- function(pValues, alpha, silent=FALSE) { m <- length(pValues) pi0.ABH <- ABH_pi0_est(pValues)$pi0 criticalValues <- sapply(1:m, function(i) (i*alpha)/(m*pi0.ABH)) adjPValues <- p.adjust(pValues,"BH")*pi0.ABH rejected <- (adjPValues <= alpha) if (! silent) { cat("\n\n\t\tBenjamini-Hochberg (2000) adaptive step-up procedure\n\n") printRejected(rejected, pValues, adjPValues) } return(list(adjPValues=adjPValues, criticalValues=criticalValues, rejected=rejected, pi0=pi0.ABH, errorControl = new(Class='ErrorControl',type="FDR",alpha=alpha))) } mutoss.adaptiveBH <- function() { return(new(Class="MutossMethod", label="Benjamini-Hochberg (2000) adaptive step-up", errorControl="FDR", callFunction="adaptiveBH", output=c("adjPValues", "criticalValues", "rejected", "pi0", "errorControl"), assumptions=c("independence or positive regression dependency"), info="

Benjamini-Hochberg (2000) adaptive linear step-up procedure

\n\n\

Reference:

\

The adaptive Benjamini-Hochberg step-up procedure is applied to pValues. It controls the FDR at level alpha for independent or positive regression dependent test statistics. In the adaptive Benjamini-Hochberg step-up procedure the number of true null hypotheses is estimated first as in Hochberg and Benjamini (1990), and this estimate is used in the procedure of Benjamini and Hochberg (1995) with alpha'=alpha*m/m0. The method for estimating m0 is motivated by the graphical approach proposed by Schweder and Spjotvoll (1982), as developed and presented in Hochberg and Benjamini (1990).

\n", parameters=list(pValues=list(type="numeric"), alpha=list(type="numeric")) )) } #---------------------- Adaptive STS-----------------# adaptiveSTS <- function(pValues, alpha, lambda=0.5, silent=FALSE) { m <- length(pValues) adjP <- p.adjust(pValues,"BH") pi0 <- storey_pi0_est(pValues, lambda)$pi0 criticalValues <- sapply(1:m, function(i) (i*alpha)/(m*pi0)) adjPValues <- adjP*min(pi0, 1) rejected <- (adjPValues <= alpha) if (! silent) { cat("\n\n\t\tStorey-Taylor-Siegmund (2004) adaptive step-up procedure\n\n") printRejected(rejected, pValues, adjPValues) } return(list(adjPValues=adjPValues, criticalValues=criticalValues, rejected=rejected, pi0=pi0, errorControl = new(Class='ErrorControl',type="FDR",alpha=alpha))) } mutoss.adaptiveSTS <- function() { return(new(Class="MutossMethod", label="Storey-Taylor-Siegmund (2004) adaptive step-up", errorControl="FDR", callFunction="adaptiveSTS", output=c("adjPValues", "criticalValues", "rejected", "pi0", "errorControl"), assumptions=c("test independence or positive regression dependency"), info="

Storey-Taylor-Siegmund (2004) adaptive step-up procedure

\n\n

The adaptive STS method uses a conservative estimate of pi0 which is plugged in a linear step-up procedure. The estimation of pi0 requires a parameter λ which is set to 0.5 by default. Note that the estimated pi0 is truncated at 1 as suggested by the author, so the implemetation of the procedure is not entirely supported by the proof in the reference.

\n

Reference:

", parameters=list(pValues=list(type="numeric"), alpha=list(type="numeric"), lambda=list(type="numeric", default = 0.5)) )) } #---------------------------- Sidack Step Down--------------------------------# SidakSD <- function(pValues, alpha, silent=FALSE) { m <- length(pValues) criticalValues <- sapply(1:m, function(i) 1-(1-alpha)^(1/(m-i+1))) #rejected <- SD(pValues, criticalValues) tmp <- mt.rawp2adjp(pValues, "SidakSD") adjPValues <- tmp$adjp[order(tmp$index),2] rejected <- (adjPValues <= alpha) if (! silent) { cat("\n\n\tSidak-like (1987) step-down procedure\n\n") printRejected(rejected, pValues, NULL) } return(list(adjPValues=adjPValues, criticalValues=criticalValues, rejected=rejected, errorControl = new(Class='ErrorControl',type="FWER",alpha=alpha))) } mutoss.SidakSD <- function() { return(new(Class="MutossMethod", label="Sidak-like (1987) step-down", errorControl="FWER", callFunction="SidakSD", output=c("adjPValues", "criticalValues", "rejected", "errorControl"), assumptions=c("test independence","positive orthant dependent test statistics"), info="

Sidak-like (1987) step-down procedure

\n\n\

Reference:

\

The Sidak-like step-down procedure is an improvement over Holm's (1979) step-down procedure. The improvement is analogous to the Sidak's correction over the original Bonferroni procedure. This Sidak-like step-down procedure assumes positive orthant dependent test statistics.

\n", parameters=list(pValues=list(type="numeric"), alpha=list(type="numeric")) )) } #----------------------------------Blanchard Roquain 2008 ----------------------# indepBR <- function(pValues, alpha, lambda=1, silent = FALSE) { if ( lambda <= 0 || lambda >= 1/alpha) { stop('indepBR() : lambda should belong to (0, 1/alpha)') } len <- length(pValues) criticalValues <- sapply( 1:len, function(i) alpha * min( i * ( 1 - lambda * alpha) / (len - i + 1) , lambda ) ) rejected <- SU(pValues, criticalValues) if (! silent) { cat("\n\n\t\t Blanchard-Roquain 1-stage step-up under independence (2009)\n\n") printRejected(rejected, pValues) } return(list(rejected = rejected, criticalValues = criticalValues, errorControl = new(Class='ErrorControl',type="FDR",alpha=alpha))) } mutoss.indepBR <- function() { return(new(Class="MutossMethod", label="Blanchard-Roquain adaptive step-up (2009)", errorControl="FDR", callFunction="indepBR", output=c("criticalValues", "rejected", "errorControl"), assumptions=c("p-value independence"), info="

Blanchard-Roquain (2009) 1-stage adaptive step-up

\n\n\

Reference:

\

This is a step-up procedure with critical values\n\ Ci = alpha * min( i * ( 1 - lambda * alpha) / (m - i + 1) , lambda )\n\ where alpha is the level at which FDR should be controlled and lambda an \ arbitrary parameter belonging to (0, 1/alpha) with default value 1. \ This procedure controls FDR at the desired level when the p-values are independent.

", parameters=list(pValues=list(type="numeric"), alpha=list(type="numeric"), lambda=list(type="numeric", default=1)) )) } #----------------------------Blanchard Roquain 2009---------------------# twostageBR <- function(pValues, alpha, lambda=1, silent = FALSE) { if ( lambda <= 0 || lambda >= 1/alpha) { stop('twostageBR() : lambda should belong to (0, 1/alpha)') } m <- length(pValues) stage1 <- indepBR( pValues, alpha, lambda, silent = TRUE) pi0inv <- ( 1 - lambda*alpha )*m / ( m + 1 - sum(stage1$rejected) ) BHadjPValues <- p.adjust(pValues,"BH") rejected <- ( BHadjPValues <= alpha*pi0inv ) if (! silent) { cat("\n\n\t\tBlanchard-Roquain (2009) 2-stage step-up Procedure\n\n") printRejected(rejected, pValues) } return(list(rejected=rejected, errorControl = new(Class='ErrorControl',type="FDR",alpha=alpha))) } mutoss.twostageBR <- function() { return(new(Class="MutossMethod", label="Blanchard-Roquain 2-stage adaptive step-up (2009)", errorControl="FDR", callFunction="twostageBR", output=c("rejected", "errorControl"), assumptions=c("p-value independence"), info="

Blanchard-Roquain 2-stage step-up under independence

\n\n\

Reference:

\ \

This is an adaptive linear step-up procedure where the proportion of true\ nulls is estimated using the Blanchard-Roquain 1-stage procedure with parameter lambda,\ via the formula\n\ estimated pi0 = ( m - R(alpha,lambda) + 1) / ( m*( 1 - lambda * alpha ) )\n\ where R(alpha,lambda) is the number of hypotheses rejected by the BR 1-stage procedure, alpha is the level at which FDR should be controlled and lambda an arbitrary parameter belonging to (0, 1/alpha) with default value 1. This procedure controls FDR at the desired level when the p-values are independent.

", parameters=list(pValues=list(type="numeric"), alpha=list(type="numeric"), lambda=list(type="numeric",default=1)) )) } mutoss/R/MuTossObject.R0000644000176200001440000000357215123457163014527 0ustar liggesuserssetClass("ErrorControl", representation = representation( type = "character", # Type of error rate controlled for (FWER, FWER.weak, FDR, FDX, gFWER, perComparison (?)) alpha = "numeric", # Error rate of the procedure k = "numeric", # Additional parameter for generalized FWE control q = "numeric" # Additional paramter for FDX control ) ) ##TODO: add slots for "model": formula, link, family ##TODO: write some header, it is the main object that comes out after four weeks of hard work!! setClass("Mutoss", representation = representation( data = "ANY", # Raw data used in model model = "ANY", # link function,error family and design description = "character", statistic = "numeric", # For Z, T or F statistics (maybe different slots?) hypotheses = "ANY", hypNames = "character", # Identifiers for the hypotheses tested criticalValues = "numeric", # Procedure-specific critical values pValues = "numeric", # Raw p-values. Either imported or calculated with data and model adjPValues = "numeric", # Procedure-specific adjusted p-values errorControl = "ErrorControl", # Details of the multiplicity control procedure used. rejected = "logical", # Logical vector of the output of a procedure at a given error rate qValues = "numeric", # Storey's estimates of the supremum of the pFDR locFDR = "numeric", # Efron's local fdr estimates (by which method?) pi0 = "numeric", # Estimate of the proportion of null hypotheses (by which method?) confIntervals = "matrix", # Confidence intervals for selected parameters (of which kind? selected how?) commandHistory = "character" ) ) mutoss/R/multcomp.R0000644000176200001440000000411415123457163013777 0ustar liggesusers# Simultaneous confidence intervals for linear contrasts # # Author: FrankKonietschke ############################################################################### multcomp.wrapper <- function(model, hypotheses, alternative, rhs=0, alpha, factorC ) { type<-"" if (any(factorC== c("Tukey", "Dunnett", "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus", "UmbrellaWilliams"))) { eval(parse(text=paste("type <- mcp(",factorC,"=hypotheses)"))) } else { eval(parse(text=paste("type <- mcp(",factorC,"=hypotheses)"))) } glhtObj <- glht(model, linfct = type, rhs=rhs, alternative=alternative) summaryGLHT <- summary(glhtObj) pvalues <- summaryGLHT$test$pvalues estimates <- summaryGLHT$test$coefficients confint <- confint(glhtObj,level=(1-alpha))$confint rejected1 <- (pvalues < alpha) confi <- cbind(confint) print(cbind(confi,pvalues)) return(list(adjPValues=pvalues,rejected=rejected1,confIntervals= confi, errorControl = new(Class='ErrorControl',type="FWER",alpha=alpha))) } mutoss.multcomp<- function() { return(new(Class="MutossMethod", label="Multiple Contrast Tests", errorControl="FWER", callFunction="multcomp.wrapper", output=c("adjPValues", "rejected","confIntervals","errorControl"), info="

Parametric multiple contrast tests and simultaneous confidence intervals

With this function, it is possible to compute simultaneous tests and confidence intervals for general linear hypotheses in parametric models

Reference:

", parameters=list(model=list(type="ANY"), hypotheses=list(type="ANY"), alpha=list(type="numeric"), alternative=list(type="character", label="Alternative", choices=c("two.sided", "less", "greater")), factorC=list(type="character", label="Factor for Comparison", fromR="FactorVar") ) )) } mutoss/R/marginal.R0000644000176200001440000003175315123457163013742 0ustar liggesusers# TODO: Add comment # # Author: wiebke ############################################################################### #### one sample model #### onesamp.model <- function() { return(list(model=list(typ="onesamp"))) } mutoss.onesamp.model <- function() { return(new(Class="MutossMethod", label="One-sample test", callFunction="onesamp.model", output=c("model"), info="

One sample model

The input for this one sample model is a data matrix whose columns represent the samples and the rows represent the multiple endpoints. E.g. for genomics this would be a gene matrix, where each row gives the expression for a single gene.

For the next steps you have the following choices:

Either marginal hypotheses tests (if robust Wilcoxon otherwise t-test) could be performed on each row of the data matrix to obtain raw p-values which then need to be adjusted for multiplicity to control a chosen error rate.

Or resampling based methods could be performed based on Dudoit and van der Laan (2007) to obtain adjusted p-values which control the FWER. Afterwards it is possible to use augmentation procedures to get adjusted p-values for control of FDR, FDX or gFWER.

Reference:

", parameters=list( ) )) } onesamp.marginal <- function(data, robust, alternative, psi0) { result <- NULL if (robust) { result <- apply(data, 1, function(x) {wilcox.test(x, alternative=alternative, mu=psi0)$p.value} ) } else { result <- apply(data, 1, function(x) {t.test(x ,alternative=alternative, mu=psi0)$p.value} ) } return(list(pValues=result)) } mutoss.onesamp.marginal.model <- function(model) { return("typ" %in% names(model) && model$typ == "onesamp") } mutoss.onesamp.marginal <- function() { return(new(Class="MutossMethod", label="One-sample test", callFunction="onesamp.marginal", output=c("pValues"), info="

Marginal one sample test.

The robust version uses the Wilcoxon-Mann-Whitney test, otherwise a t-test will be performed.

Reference:

", parameters=list( data=list(type="ANY"), robust=list(type="logical", label="Robust statistic"), alternative=list(type="character", label="Alternative", choices=c("two.sided", "less", "greater")), psi0=list(type="numeric", label="Hypothesized null value", default=0) ) )) } #### two sample model #### twosamp.model <- function(classlabel) { classlabel <- as.vector(classlabel) return(list(model=list(typ="twosamp", classlabel=classlabel))) } mutoss.twosamp.model <- function() { return(new(Class="MutossMethod", label="Two sample test", callFunction="twosamp.model", output=c("model"), info="

Two sample model

The input for this one sample model is a data matrix whose columns represent the samples and the rows represent the multiple endpoints. E.g. for genomics this would be a gene matrix, where each row gives the expression for a single gene.

Furthermore, a classlabel needs to be provided to distinguish the two sample groups.

For the next steps you have the following choices:

Either marginal hypotheses tests (if robust Wilcoxon otherwise t-test) could be performed on each row of the data matrix to obtain raw p-values which then need to be adjusted for multiplicity to control a chosen error rate.

Or resampling based methods could be performed based on Dudoit and van der Laan (2007) to obtain adjusted p-values which control the FWER. Afterwards it is possible to use augmentation procedures to get adjusted p-values for control of FDR, FDX or gFWER.

Reference:

", parameters=list( classlabel=list(type="RObject", label="classlabel") ) )) } twosamp.marginal <- function(data, model, robust, alternative, psi0, equalvar) { label <- as.numeric(as.factor(model$classlabel)) result <- NULL if (robust) { result <- apply(data, 1, function(x) {wilcox.test(x=x[ ,label==1], y=x[label==2], alternative=alternative, mu=psi0)$p.value} ) } else { result <- apply(data, 1, function(x) {t.test(x=x[ ,label==1], y=x[label==2], alternative=alternative, mu=psi0, equal.var=equalvar)$p.value} ) } return(list(pValues=result)) } mutoss.twosamp.marginal.model <- function(model) { return("typ" %in% names(model) && model$typ == "twosamp") } mutoss.twosamp.marginal <- function() { return(new(Class="MutossMethod", label="Two sample test", callFunction="twosamp.marginal", output=c("pValues"), info="

The robust version uses the Wilcoxon-Mann-Whitney test, otherwise a two-sample t-test will be performed.

Reference:

", parameters=list( data=list(type="ANY"), model=list(type="ANY"), robust=list(type="logical", label="Robust statistic"), alternative=list(type="character", label="Alternative", choices=c("two.sided", "less", "greater")), psi0=list(type="numeric", label="Hypothesized null value", default=0), equalvar=list(type="logical", label="Equal variance") ) )) } ### paired sample model ### paired.model <- function(classlabel) { classlabel <- as.vector(classlabel) return(list(model=list(typ="pairedsamp", classlabel=classlabel))) } mutoss.paired.model <- function() { return(new(Class="MutossMethod", label="Paired sample test", callFunction="paired.model", output=c("model"), info="

Paired sample test

The robust version uses the Wilcoxon signed rank test, otherwise a paired t-test will be performed.

The input for this paired sample model is a data matrix whose columns represent the samples and the rows represent the multiple endpoints. E.g. for genomics this would be a gene matrix, where each row gives the expression for a single gene.

Furthermore, a classlabel needs to be provided to distinguish the two paired groups. The arrangement of group indices does not matter, as long as the columns are arranged in the same corresponding order between groups. For example, if group 1 is code as 0 and group 2 is coded as 1, for 3 pairs of data, it does not matter if the classlabel is coded as (0,0,0,1,1,1) or (1,1,1,0,0,0) or (0,1,0,1,0,1) or (1,0,1,0,1,0), the paired differences between groups will be calculated as group2 - group1.

For the next steps you have the following choices:

Either marginal hypotheses tests (if robust Wilcoxon otherwise t-test) could be performed on each row of the data matrix to obtain raw p-values which then need to be adjusted for multiplicity to control a chosen error rate.

Or resampling based methods could be performed based on Dudoit and van der Laan (2007) to obtain adjusted p-values which control the FWER. Afterwards it is possible to use augmentation procedures to get adjusted p-values for control of FDR, FDX or gFWER.

Reference:

", parameters=list( classlabel=list(type="RObject", label="classlabel") ) )) } paired.marginal <- function(data, model, robust, alternative, psi0, equalvar) { label <- as.numeric(as.factor(model$classlabel)) result <- NULL if (robust) { result <- apply(data, 1, function(x) {wilcox.test(x=x[label==1], y=x[label==2], alternative=alternative, mu=psi0, paired=TRUE, var.equal=equalvar)$p.value} ) } else { result <- apply(data, 1, function(x) {t.test(x=x[label==1], y=x[label==2], alternative=alternative, mu=psi0, paired=TRUE, var.equal=equalvar)$p.value} ) } return(list(pValues=result)) } mutoss.paired.marginal <- function() { return(new(Class="MutossMethod", label="Paired sample test", callFunction="paired.marginal", output=c("pValues"), info="

The robust version uses the Wilcoxon test, otherwise a paired t-test will be performed.

A vector of classlabels needs to be provided to distinguish the two paired groups. The arrangement of group indices does not matter, as long as the columns are arranged in the same corresponding order between groups. For example, if group 1 is code as 0 and group 2 is coded as 1, for 3 pairs of data, it does not matter if the classlabel is coded as (0,0,0,1,1,1) or (1,1,1,0,0,0) or (0,1,0,1,0,1) or (1,0,1,0,1,0), the paired differences between groups will be calculated as group2 - group1.

You could either choose a valid R object to load as classlabels or you could provide it manually by inserting e.g. c(0,1,0,1,0,1) or rep(c(0,1), each=5) or rep(c(0,1), 5).

", parameters=list( data=list(type="ANY"), model=list(type="ANY"), robust=list(type="logical", label="Robust statistic"), alternative=list(type="character", label="Alternative", choices=c("two.sided", "less", "greater")), psi0=list(type="numeric", label="Hypothesized null value", default=0), equalvar=list(type="logical", label="Equal variance") ) )) } mutoss.paired.marginal.model <- function(model) { return("typ" %in% names(model) && model$typ == "pairedsamp") } ### f test model ### ftest.model <- function(classlabel) { classlabel <- as.vector(classlabel) return(list(model=list(typ="ftest", classlabel=classlabel))) } mutoss.ftest.model <- function() { return(new(Class="MutossMethod", label="F test", callFunction="ftest.model", output=c("model"), info="

F test

The input for this F test model is a data matrix whose columns represent the samples and the rows represent the multiple endpoints. E.g. for genomics this would be a gene matrix, where each row gives the expression for a single gene.

Furthermore, a classlabel needs to be provided to distinguish k sample groups.

For the next steps you have the following choices:

Either marginal hypotheses tests (if robust Kruskal-Wallis test, otherwise F-test) could be performed on each row of the data matrix to obtain raw p-values which then need to be adjusted for multiplicity to control a chosen error rate.

Or resampling based methods could be performed based on Dudoit and van der Laan (2007) to obtain adjusted p-values which control the FWER. Afterwards it is possible to use augmentation procedures to get adjusted p-values for control of FDR, FDX or gFWER.

Reference:

", parameters=list( classlabel=list(type="RObject", label="classlabel") ) )) } ftest.marginal <- function(data, model, robust) { label <- as.numeric(as.factor(model$classlabel)) result <- NULL if (robust) { result <- apply(data, 1, function(x) {kruskal.test(x=x, g=label)$p.value} ) } else { result <- apply(data, 1, function(x) { out=x anova(lm( out ~ label ))$'Pr(>F)'[1]} ) } return(list(pValues=result)) } mutoss.ftest.marginal <- function() { return(new(Class="MutossMethod", label="F test", callFunction="ftest.marginal", output=c("pValues"), info="

Robust = Kruskal-Wallis test. Otherwise F-test.

Reference:

", parameters=list( data=list(type="ANY"), model=list(type="ANY"), robust=list(type="logical", label="Robust statistic") ) )) } mutoss.ftest.marginal.model <- function(model) { return("typ" %in% names(model) && model$typ == "ftest") } mutoss/R/multtest.R0000644000176200001440000004125215123457163014024 0ustar liggesusers####################################################################### ########### here are the multtest methods - marginal siblings can be found in marginal.R ###################### ### one sample model with multtest ### onesamp.multtest <- function(data, alternative, robust, psi0, alpha, nulldist, B=1000, method, seed=12345) { result <- MTP(X=data, W = NULL, Y = NULL, Z = NULL, Z.incl = NULL, Z.test = NULL, na.rm = TRUE, test = "t.onesamp", robust = robust, standardize = TRUE, alternative = alternative, psi0 = psi0, typeone = "fwer", k = 0, q = 0.1, fdr.method = "restricted", alpha = alpha, smooth.null = FALSE, nulldist = nulldist, B = B, ic.quant.trans = FALSE, MVN.method = "mvrnorm", penalty = 1e-06, method = method, get.cr = FALSE, get.cutoff = FALSE, get.adjp = TRUE, keep.nulldist = FALSE, keep.rawdist = FALSE, seed = seed, cluster = 1, type = NULL, dispatch = NULL, marg.null = NULL, marg.par = NULL, keep.margpar = TRUE, ncp = NULL, perm.mat = NULL, keep.index = FALSE, keep.label = FALSE) return(list(adjPValues=result@adjp, rejected=as.vector(result@reject))) } mutoss.onesamp.multtest.model <- function(model) { return("typ" %in% names(model) && model$typ == "onesamp") } mutoss.onesamp.multtest <- function() { return(new(Class="MutossMethod", label="Resampling-based one sample test", errorControl="FWER", callFunction="onesamp.multtest", output=c("adjPValues", "rejected"), info="

Resampling-based one sample test

There are different choices of resampling methods available for estimating the joint test statistics null distribution:\n\

There are four adjustment methods to control the FWER:

The default number of bootstrap iterations (or number of permutations if resampling method is \"perm\") is 1000. This can be reduced to increase the speed of computations, at a cost to precision. However, it is recommended to use a large number of resampling iterations, e.g. 10,000.

The robust version uses the Wilcoxon-Mann-Whitney test, otherwise a t-test will be performed.

Reference:

", parameters=list( data=list(type="ANY"), model=list(type="ANY"), robust=list(type="logical", label="Robust statistic"), alternative=list(type="character", label="Alternative", choices=c("two.sided", "less", "greater")), psi0=list(type="numeric", label="Hypothesized null value", default=0), alpha=list(type="numeric"), nulldist=list(type="character", label="Resampling Method", choices=c("boot.cs", "boot.ctr", "boot.qt", "perm")), B=list(type="numeric", label="Number of Resampling Iterations", default=1000), method=list(type="character", label="Adjustment Method", choices=c("sd.minP","sd.maxT","ss.minP","ss.maxT"), seed=list(type="ANY", default=12345) ) ))) } ### paired sample model with multtest ### paired.multtest <- function(data, model, alternative, robust, psi0, alpha, nulldist, B=1000, method, seed=12345) { result <- MTP(X=data, W = NULL, Y = model$classlabel, Z = NULL, Z.incl = NULL, Z.test = NULL, na.rm = TRUE, test = "t.pair", robust = robust, standardize = TRUE, alternative = alternative, psi0 = psi0, typeone = "fwer", k = 0, q = 0.1, fdr.method = "restricted", alpha = alpha, smooth.null = FALSE, nulldist = nulldist, B = B, ic.quant.trans = FALSE, MVN.method = "mvrnorm", penalty = 1e-06, method = method, get.cr = FALSE, get.cutoff = FALSE, get.adjp = TRUE, keep.nulldist = FALSE, keep.rawdist = FALSE, seed = seed, cluster = 1, type = NULL, dispatch = NULL, marg.null = NULL, marg.par = NULL, keep.margpar = TRUE, ncp = NULL, perm.mat = NULL, keep.index = FALSE, keep.label = FALSE) return(list(adjPValues=result@adjp, rejected=as.vector(result@reject))) } mutoss.paired.multtest.model <- function(model) { return("typ" %in% names(model) && model$typ == "pairedsamp") } mutoss.paired.multtest <- function() { return(new(Class="MutossMethod", label="Resampling-based paired sample test", errorControl="FWER", callFunction="paired.multtest", output=c("adjPValues", "rejected"), info="

Resampling-based paired test

There are different choices of resampling methods available for estimating the joint test statistics null distribution:\n\

There are four adjustment methods to control the FWER:

The default number of bootstrap iterations (or number of permutations if resampling method is \"perm\") is 1000. This can be reduced to increase the speed of computations, at a cost to precision. However, it is recommended to use a large number of resampling iterations, e.g. 10,000.

The robust version uses the Wilcoxon-Mann-Whitney test, otherwise a t-test will be performed.

Reference:

", parameters=list( data=list(type="ANY"), model=list(type="ANY"), robust=list(type="logical", label="Robust statistic"), alternative=list(type="character", label="Alternative", choices=c("two.sided", "less", "greater")), psi0=list(type="numeric", label="Hypothesized null value", default=0), alpha=list(type="numeric"), nulldist=list(type="character", label="Resampling Method", choices=c("boot.cs", "boot.ctr", "boot.qt", "perm")), B=list(type="numeric", label="Number of Resampling Iterations", default=1000), method=list(type="character", label="Adjustment Method", choices=c("sd.minP","sd.maxT","ss.minP","ss.maxT"), seed=list(type="ANY", default=12345)) ) )) } ### two sample model with multtest #### twosamp.multtest <- function(data, model, alternative, robust, psi0, equalvar, alpha, nulldist, B=1000, method, seed=12345) { if (equalvar) { result <- MTP(X=data, W = NULL, Y = model$classlabel, Z = NULL, Z.incl = NULL, Z.test = NULL, na.rm = TRUE, test = "t.twosamp.equalvar", robust = robust, standardize = TRUE, alternative = alternative, psi0 = psi0, typeone = "fwer", k = 0, q = 0.1, fdr.method = "restricted", alpha = alpha, smooth.null = FALSE, nulldist = nulldist, B = B, ic.quant.trans = FALSE, MVN.method = "mvrnorm", penalty = 1e-06, method = method, get.cr = FALSE, get.cutoff = FALSE, get.adjp = TRUE, keep.nulldist = FALSE, keep.rawdist = FALSE, seed = seed, cluster = 1, type = NULL, dispatch = NULL, marg.null = NULL, marg.par = NULL, keep.margpar = TRUE, ncp = NULL, perm.mat = NULL, keep.index = FALSE, keep.label = FALSE) } else { result <- MTP(X=data, W = NULL, Y = model$classlabel, Z = NULL, Z.incl = NULL, Z.test = NULL, na.rm = TRUE, test = "t.twosamp.unequalvar", robust = robust, standardize = TRUE, alternative = alternative, psi0 = psi0, typeone = "fwer", k = 0, q = 0.1, fdr.method = "restricted", alpha = alpha, smooth.null = FALSE, nulldist = nulldist, B = B, ic.quant.trans = FALSE, MVN.method = "mvrnorm", penalty = 1e-06, method = method, get.cr = FALSE, get.cutoff = FALSE, get.adjp = TRUE, keep.nulldist = FALSE, keep.rawdist = FALSE, seed = seed, cluster = 1, type = NULL, dispatch = NULL, marg.null = NULL, marg.par = NULL, keep.margpar = TRUE, ncp = NULL, perm.mat = NULL, keep.index = FALSE, keep.label = FALSE) } return(list(adjPValues=result@adjp, rejected=as.vector(result@reject))) } mutoss.twosamp.multtest.model <- function(model) { return("typ" %in% names(model) && model$typ == "twosamp") } mutoss.twosamp.multtest <- function() { return(new(Class="MutossMethod", label="Resampling-based two sample test", errorControl="FWER", callFunction="twosamp.multtest", output=c("adjPValues", "rejected"), info="

Resampling-based two sample test

There are different choices of resampling methods available for estimating the joint test statistics null distribution:\n\

There are four adjustment methods to control the FWER:

The default number of bootstrap iterations (or number of permutations if resampling method is \"perm\") is 1000. This can be reduced to increase the speed of computations, at a cost to precision. However, it is recommended to use a large number of resampling iterations, e.g. 10,000.

The robust version uses the Wilcoxon-Mann-Whitney test, otherwise a t-test will be performed.

Reference:

", parameters=list( data=list(type="ANY"), model=list(type="ANY"), robust=list(type="logical", label="Robust statistic"), alternative=list(type="character", label="Alternative", choices=c("two.sided", "less", "greater")), psi0=list(type="numeric", label="Hypothesized null value", default=0), equalvar=list(type="logical", label="Equal variance"), alpha=list(type="numeric"), nulldist=list(type="character", label="Resampling Method", choices=c("boot.cs", "boot.ctr", "boot.qt", "perm")), B=list(type="numeric", label="Number of Resampling Iterations", default=1000), method=list(type="character", label="Adjustment Method", choices=c("sd.minP","sd.maxT","ss.minP","ss.maxT"), seed=list(type="ANY", default=12345)) ) )) } ##################### F test ############################################# ########################################################################### ftest.multtest <- function(data, model, robust, alpha, nulldist, B=1000, method, seed=12345) { result <- MTP(X=data, W = NULL, Y = model$classlabel, Z = NULL, Z.incl = NULL, Z.test = NULL, na.rm = TRUE, test = "f", robust = robust, standardize = TRUE, typeone = "fwer", alpha = alpha, smooth.null = FALSE, nulldist = nulldist, B = B, ic.quant.trans = FALSE, MVN.method = "mvrnorm", penalty = 1e-06, method = method, get.cr = FALSE, get.cutoff = FALSE, get.adjp = TRUE, keep.nulldist = FALSE, keep.rawdist = FALSE, seed = seed, cluster = 1, type = NULL, dispatch = NULL, marg.null = NULL, marg.par = NULL, keep.margpar = TRUE, ncp = NULL, perm.mat = NULL, keep.index = FALSE, keep.label = FALSE) return(list(adjPValues=result@adjp, rejected=as.vector(result@reject))) } mutoss.ftest.multtest <- function() { return(new(Class="MutossMethod", label="Resampling-based F test", errorControl="FWER", callFunction="ftest.multtest", output=c("adjPValues", "rejected"), info="

Resampling-based F test

There are different choices of resampling methods available for estimating the joint test statistics null distribution:\n\

There are four adjustment methods to control the FWER:

The default number of bootstrap iterations (or number of permutations if resampling method is \"perm\") is 1000. This can be reduced to increase the speed of computations, at a cost to precision. However, it is recommended to use a large number of resampling iterations, e.g. 10,000.

The robust version uses the Kruskal-Wallis test, otherwise a F test will be performed.

Reference:

", parameters=list( data=list(type="ANY"), model=list(type="ANY"), robust=list(type="logical", label="Robust statistic"), alpha=list(type="numeric"), nulldist=list(type="character", label="Resampling Method", choices=c("boot.cs", "boot.ctr", "boot.qt", "perm")), B=list(type="numeric", label="Number of Resampling Iterations", default=1000), method=list(type="character", label="Adjustment Method", choices=c("sd.minP","sd.maxT","ss.minP","ss.maxT"), seed=list(type="ANY", default=12345) ))) )} mutoss.ftest.multtest.model <- function(model) { return("typ" %in% names(model) && model$typ == "ftest") }mutoss/R/Tukey.R0000644000176200001440000000404715123457163013245 0ustar liggesusers# Tukey HSD Test in parametric factorial designs # # Author: FrankKonietschke ############################################################################### tukey.wrapper <- function(model, alpha,factorC) { model <- aov(model) tukeyObj <- TukeyHSD(model,conf.level=1-alpha, factorC) estimates <- c(tukeyObj)[[1]][,1] confintL <- c(tukeyObj)[[1]][,2] confintU <- c(tukeyObj)[[1]][,3] confi <- cbind(estimates, confintL, confintU) pvalues <- c(tukeyObj)[[1]][,4] rejected1 <- (pvalues < alpha) #confi <- cbind(confint) print(cbind(confi,pvalues)) return(list(adjPValues=pvalues,rejected=rejected1,confIntervals= confi, errorControl = new(Class='ErrorControl',type="FWER",alpha=alpha))) } #tukey.wrapper(aov(breaks~wool*tension,data=warpbreaks),alpha=0.05,"tension") mutoss.tukey<- function() { return(new(Class="MutossMethod", label="Tukey HSD Test", errorControl="FWER", callFunction="tukey.wrapper", output=c("adjPValues", "rejected","confIntervals","errorControl"), info="

Tukey HSD test and simultaneous confidence intervals in parametric factorial designs

'With this function, it is possible to compute all pairs comparisons for expectations and simultaneous confidence intervals in factorial linear models. Hereby, the all-pairs comparisons can be performed for user given effects. The overall variance is estimated by the linear model as well as the degree of freedom used by the studentized range distribution.

Reference:

", parameters=list(model=list(type="ANY"), alpha=list(type="numeric"), factorC=list(type="character", label="Factor for Comparison", fromR="FactorVar") ) )) } mutoss/R/Rank_Truncated.R0000644000176200001440000000305115123457163015042 0ustar liggesusers# # # Author: FrankKonietschke ############################################################################### ranktruncated <- function(pValues, K, silent = FALSE){ L <- length(pValues) if (L>1000) { warning("Method implementation is numerical instable for large numbers of p-values. Please do not rely on results.") } if (K > L){ warn1 <- paste("K must be smaller than L") stop(warn1) } #-----Compute the test statistic-----# index <- order(pValues) rindex <- order(index) spval <- pValues[index] w <- prod(spval[1:K]) #---Compute the used pvalues--------# w.pvalues <- pValues[rindex] p.used <- data.frame(Position=index[1:K], pValue=spval[1:K]) #--Compute the function awt as in the paper-----# awt <- function(w,t,K){ if (w<=t^K){ s <- c(0:(K-1)) num1 <- K*log(t)-log(w) num2 <- w * sum(num1^s/factorial(s)) } if (w > t^K) { num2<- t^K } return(num2) } # ----- Compute now the exact distribution-----# fac1 <- choose(L, K+1)*(K+1) t <- seq(0.001,0.999,0.001) terg <- c() for (i in 1:length(t)){ terg[i] <- (1-t[i])^(L-K-1)*awt(w,t[i],K) } #--------------Compute the p-Value -------------# distribution <- fac1*mean(terg) #-Regarding the error of numerical integration-# p1 <- (distribution>1) distribution[p1] <- 1 #--------------Prepare the output---------------# if (! silent) { cat("#----Rank Truncated Product of P-Values (Dubridge and Koeleman; 2003) \n\n") } result <- data.frame(Statistic = w, p.Value=distribution) return(list(Used.pValue=p.used, RTP=result)) } mutoss/NEWS0000644000176200001440000000215715123457163012317 0ustar liggesusersVersion 0.1-14 ============== - fixed CRAN notes and warnings Version 0.1-13 ============== - ranktruncated warns for large numbers of p-values. (Thanks to Phillip Bredahl Mogensen for notification!) - noted that adaptiveBH is not equivalent to multcomp's ABH. (Thanks to David Swanepoel for notification!) Version 0.1-12 ============== - changed description according to CRAN policy Version 0.1-11 ============== - fixed CRAN notes and warnings - switched from RUnit to testthat Version 0.1-10 ============== - compatibility with qvalue 1.99.0 Version 0.1-9 ============= - compatibility to qvalue 1.43.0 Version 0.1-8 ============= - moving many packages from DEPENDS to IMPORTS - fixed layout in examples - fixed various warnings Version 0.1-7 ============= - corrections in the Hommel documentation - package multtest is a dependency Version 0.1-6 ============= - simulation Tool: Objects returned by simulation() use less memory and generated data can be discarded. Version 0.1-5 ============= - jointCDF.orderedUnif which was only a helper function is now exported Version 0.1-4 ============= - Bug fix in ABH_pi0_estmutoss/vignettes/0000755000176200001440000000000015123457212013616 5ustar liggesusersmutoss/vignettes/simToolManual.Rnw0000644000176200001440000000033215123457163017075 0ustar liggesusers% \VignetteIndexEntry{MuToss Simulation Tool Guide} \documentclass[a4paper]{article} \usepackage{hyperref} \usepackage{pdfpages} \begin{document} \includepdf[fitpaper=true,pages=-]{simToolVignette.pdf} \end{document} mutoss/vignettes/simToolVignette.pdf0000644000176200001440000064030115123457163017456 0ustar liggesusers%PDF-1.5 %¿÷¢þ 1 0 obj << /Type /ObjStm /Length 3466 /Filter /FlateDecode /N 99 /First 790 >> stream xœí[[sG~ß_Ño!•BÓ÷ËV*Uã@À@lLƒ<ÆJdI‘dBö×ïwNä‘4²“­­r«§çô¹|çÒ=šF )ŒP2+´ŒÂ c´ðÂ:/‚pQ‰(‚ô"‰h$èDrN(…žMBit#pÐÑ E‰q8ƒ qÀ'b0 •ÀL%È‚0-…ÖßJhã .„¶à¯Ñu tò×ø‡›^èõt:“(Œ¤ÉIÄ…0ó‚!àBKãø8·Âx"tƒÇ·&z4Á¨b¥Ãä$¬â a55JX‚ÅâcÁć.„õÔ8aƒ¦ a#Œ¶@3ÁX <% &áÀh§’G#œøÖÂ"#œ ¸ {¡ªó ƒ ä i¢ûIxéá”ÄÀ¡žÝ‡!ÒÓããà3L0îñ\šBž>ðŸ¿ÿA… aOPø-ø¥‚Áüué¾Ã7#F¬qÀ7œ –Á !¡ ¹‘†0NQ¤pQ1¸6Ñ|ˆLñ@¡!5¤EĆô€#"bdn!£ +eA™† ꉄÀR 1“Ú¾DqèZ²0+³‘Ш+ãR œMD„¢¯à]Ѐ³5«RRÏ©}û­(†ƒ©àÂ=!MŽ¨ OÖ]Ì´u}Ý…+BÝ…ÎÜûî;Q¼»ÇÕT¼Ewÿ@'Õ§©x‡[,ê!DUƒé¨<ù°:ë•{ÃO˜ ñÏ%ס€‰VuÄ;p)ǘA ÉôGÕdx5îV¡óÀÉŸ£Š¨>T3!{å¤b›Šßìÿ|ôô›‡‡G@±8è'Ó‡å˜ÜÁVïW“î¸7šÇ„(³{VÖ4ð…(Ž¯ÞO™?IQ3a™ùëÞÙôbBžÌ¶/É>>yñúéK–­›²m‹lý¥²ÝÙG{o^?ƒì½Ó%árExŒËÂQg6nÛ…?~üêÙûY¸¼IxX.7nÚ…ÿôÃÞñ“Œú‚lDö êjE¶ÞL¶n—½÷êôôÍ#È>|r£ð˜V\¾¡pÕþpx…{^O{g¤PFEeUö¬Î3t†ZgÁ׉•ârMfÜ…êŬ24Š¯|Ÿ+ ªÑ¬h½Ü¡dè]K†R7׌ïŸ?ûáÅ)¼xr²èEÓ’¸«á»¡e{½z~šC(,FÐjî$¹B›åNHíŸ>:Ù;=ðãg+¶›UñnÅv¿™øØ.þøàùáÃ$þÍ¢plŠV„û%áz³‚BSvë·]ÚèíÓfç˜ëbš€!=&´}ašGƒîð¬7ø@ûš¬óµ‡SÆc/+AûNÊmÇÂéÆa9÷X¿ŽD•¶¬g³ÿ®áâö¼¼¬2B mI¯elž\Bé=Fgɉ¦Ý‰¶Ý‰ ÏíPvŠÓï­º™ç“K~˜ÍYuŸÛy#׺o\•ÓÞp°_N+qoÿßZbHMRØ“~] îáâpx¶––ž]u+¢º£dGað¤7íW4òý¸]ô%/®¦£«é×-+zÕÿXM{ݲAº¶¼öòçËÞzŽ÷÷†ý³õlͦlç $ƒƒIïz`¿w~^}Bû-=Á—½ÁÕ„¼’yÍI?Wnu!ó;G_57®Ùkê=RÞ¥agKW¶'«ÑtôtÿÕkZ9ŽMײfÚ¥uÃm¸d.l7¯1¾U¬âvX³Œ•Y»½YÙ'æ­”Éúšì%“CÄf.ÖßâFñ®Â+í ™Ý ‘î¦ Ìb}å7¨·³(®*¿åã{‹òk»‘^;-ÝšªÖ-ÝjËתöK–î(7\º3á],ÝÖ~ÙÒ}7kìn>6ô[Ùzo¹=kñ±û"ûM}ìoÁÇ?—£ó}`û¾„Í3¯~þ7œâÄÉÿŸ8±¿>N¶Ü¦¸•8q/½.—$—-ðcŸ}e¿ÅÒû¹0TjÃ0d»(5Îÿ/…ÐÍ;â[]"·ÜϵÕÚGû»Ùª-·S-§¿mG¢·ÜN­*ï×æðÝÀ­·ÜCµhl¶Óø‹´ÜòW×-×®¡·‚ëR¥wPyö´ÖbÝΟV¬Yÿ{Wz5¯ô>ÞF×Òl¶›¨ ý÷–ñ·ô–¸ãÄ;ô´éÈ€ÉXdB?:–&­_ è¦ÒówfoE¡™«Ãð?ýa¤ïè¼ð"îMêä”ëЀ¾µ¶céLH' krÍ&O-òÎ ã Æ] ?¹h:ûVݡ㶦vt¨ÛhT'ÒJPRiØà:HñŽîz­™³ç»4uì(žo¼&jg1_I:¦°ØhÎØÜ:\oóלæ®{ÎHXðØ\„†AYèu Q¡‘·z†HšG£ÐZ ¡¯Ãâ‘™(1’ ¯ˆÒŸl“Õ"$ºÓ)Ãhî>EF6±' Ê¼è.߉´Ð^£b•'b|¢øZõd¤ÉÏ6’¦6„ÚÆ’ÎV©šÎ "I¦ã2RHö;I6sv²~ÎqÔpa$¨Í¸Éw[Ð'\s”ñb@7°O’ôURQ …†£®]Å\÷šTMX®Zßk¨÷¥áÒ HõÜæI§l–'+r xG††’èŒWê×v"5Db2Ÿ<ÆskîÆÓ ¼æï2œt”h&ÞÑ )b}mèr¯3«½Ñè7¼Ç£ >Õ×°U°XÕ3òè Ö«îl$9 Þzæœ:SØ@¶[Ç-û¦y7c•ûìüvä5 Ž¬/õ̼—ïìÒÖsk©>’‡š¬ë–®©"£þ¦D1Ë´äÃÕœ&kÈ t «#z£3|H?$ap\e=A™ "‚^õ‘˽eŽ(Óä<Ët$X)¯ënn} s­IN¦EΞĔ²‹¿Ç?"6Ôô4:³ŸGµy$1þ5›ÛäÉ4RéI\Öé¹ëÁ¤›Ÿ¥-žcËÑãª÷á—Ñç3*ôL¯Ø+ö‹GÅ“âiñ¼8*ÞeÑ-Ίª8/>E¯è—Å ÃAUŒŠ=Q÷«óiî‰c1ªÆ½áY1.&Ť÷©˜Ó‹qUÓ?†ÅUñ±øTü§ñ4L/„I¡ûŠ4:è—&Â.¹ï¼¸¯i‰UÞòâZŸ‚9èá9ZÍ^¿ÓH~&^:ôødZö{݃ ¦7ÌÓêò'~SÑxPn¼–.NkL°ÎV7í`{à¶C÷²†îý¼óÞG| ¯Æ€ñv@üs ÁHÇ—Mj1,øtpµF™nF³{ŠÇÅOEÙ]”ˆ6‚éò 863Óè:Z¤-fÆe3—NŠ6ͼOA’ E¸ÉN£¶²s°™9jût<~Åš´lÍâ¡Å5ÆDuÛÆÌbÿQqç=)~@<ã,xÁyp\œ¯àÔ×ÅiïÇe÷·jÊ_÷sÌw‹î°? ½¼,‘8gÃ~¿#XpQý~Uö‹êS·_^RJQV-$ÖEqñçè¢ Ã~-~«³lÐC’Ý”jdç,å~/~¿ª&ô+ :Ãiuö¾Ÿéø*w9-«Õ–œäôÜ"A­œE®lIÐù¡¹¯ÏW·æ§¿9=·ôô>¼z\Wµì¢ªNR— !67 > stream xÚÝXKÛ6¾çW=I@¥ˆEIÇn›¶E‚ 1PMŠ,晴äÝÍ¿ï¼(Ó¯` 4ŦÛ5Î|óöÕüÅËkSÎTk•™Ù|9Ó¶ŒM–Ϭ5±.ìl¾˜ý m¨‚Õ<ÌË`FÚ· ºðÏù¯/¯Wq™eG–NEºˆ‹Róé×P÷ðš¾ßçuÜÁ¢ièpšEÈ&Qp6ÓÜðÙ_¦nV¡.‚Ot5ˆRXjXþˆËŒ–‰‚/ÅRev¦LœËR)mcc‹Ydt\%óþÈ7ë0UÁˆ·ðjàóVÍʸ´ÚâñdÙTÁçT•på<Ÿ±¹Ê°¡»©5Sß{Œ=ŠÒ$XÀj‡PŒ-rY£rI©…w?hRÃe™°IÙH%: ^#×tf6¨PŸ–µ‚$ â0ÊøÇ”åÅŸ'<ò2SV¡ñl­é³Ð8|”±Á«Gx¨V[òZ05 Mñ­´¾Ïþx0$—Ð_E@ÿˆ.z‚– ÁTB‘Q±ò·ñ€Dï¶[|¼åwK Î5E§JÚêç á‘ž†õáðã¡Ã44Œ’¤’b±Yâ¯>Á«Yð¾«Q銂§ðeHÅ~b}î±–_X®n ï4'ÑblöLÃä æbN0‡Pxšï“ØÇjß[· €=ë<ð5JBÄ^D$DÞvTCCn—µäIŸd{„{¾çây„ŽtTrëuÍjãEM½Át@´8Ì£ü¨Tþ½ýßaä2g¿Èu^~C—rC÷ãC§?êç¦:R@j‘q…QT¤”`›A(»q`ªeïȘêê7XàõÇ$K¨¹•…ìašeE hëLË ÆL!!j;õ¬Ji¼‘Žõ¤Ç䮵HDÉù¸;ŠhÒàývì eyXp½J›@äÑ#0UÏá‚ÏPDxZðáß ájºòc¢í©\6N’Ô‘ÌC@d3x×T5Jò9̲ ºEñЬ¹ ®vt±vCC]Q‘AÉ,«ÆuÜ3¥‚GÇ# Ü»ƒúu$ ÖVÙÙ{r &-ˆ†×õ†2ëN1UÇ”{ø4M%d÷ü7ï»’“u¨Žh±t#1j2pübcz–$§R¦ÛË)zãò‡P¿Ç5ß³«.:—3:Ê*x·FÞ°’ÃOpQ-.zCúã,æ†%È0;nÄxRúGÎ[f¾áØ8ŒpNÎY’³¥NˆŸoo†6ª5ÿ¶“]ú‰åbwÜ&"å(¢ÃížÅðqòC "Oƒ_7_F˜n:ðc wýŽ{BýÐJ¡1©e“ ;c‹‡Vìu, jÏ4$›{Ȓ˵¤)nĈ~ë|Uô…xhÒkÍôTÚÁ1оâøHUâ¶ç¡ŸN úø¦¦pœÚÇ©Ò‚¤_˜´•y¯Ç²!GfùÉý1®†sHTk*R@Zò•>BERnÏØYh§‚¬FÁù¢ñP¢ïÄ ¿X:¦+~¾¢kÿ¢~“dkùO ]æ–¾Õn©ÂÊ›˜›ÏÈß™¥aKùê[—X3¬:I,UN’Ю*r¼š’O·/í(ã¦?è~&A›NGs‚–¤Âäk÷×΂ŸýñjCž¼qyŸ¾ˆš {lR¥cU`wQ¯Å®Jj¸È¸ÜÉ‘úx„q×Wã…Qfàýq’ÜÕŸB˜“6ù´¾iÆZ¹âµ=Í[Yœ¨©¸EüßM'ÝË”r±—§ˆ5U- \Ï n÷>[“ vârÄQg‡hÀs3ýoD“à`6!tT_×”:*p ~µðgk¨·ÇxÉ-ÇcÅar„G®Ö›uälžOÕUô]:ÃõT4[׌öNIªRb|å¡71+÷%'uà¾CnQS–ì=Û­+WÝqÇ¥ú“d'ñ´‘{ÑÂ×”Ý{>s:`âq a|'ë%qö´ ',%O©ä’Ø1ŒóRá2”[»?d1Þ5öo†Šk8Ôo›¬˜E™ŽÒ®»„W/^Í_ü ¬’íjendstream endobj 102 0 obj << /Filter /FlateDecode /Length 2610 >> stream xÚå]ã¶ñý~…Ñ—ÈÀšII¤ ¤Åº‡¦¸¦@ºI’}ÐÙòÚ‰m¹Ö:—ͯÏ|‘ú°öãŠ&Å¡»¦ÈÑpf83œ½¹yõù[?+UY˜bv³ži“)Ÿš™K ¥M9»Y;K¾\Ï™.’sã’šÇÕi®Ã¸=ΦHêêG|ΓusâAý3Uû#üßuà ÓišâÈ%„+@máoYÝ˨9´ðÿ ରÇwª? 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endobj startxref 212743 %%EOF mutoss/vignettes/quickstart.Rnw0000644000176200001440000002023415123457163016506 0ustar liggesusers\documentclass[english]{article} \usepackage[T1]{fontenc} \usepackage[latin9]{inputenc} \usepackage{calc} \usepackage{babel} \usepackage{hyperref} \usepackage{url} \begin{document} % \VignetteIndexEntry{MuToss Quick Start Guide} \title{$\mu$TOSS Quick Start Guide} \author{\scriptsize {Gilles Blanchard}, % \url{gilles.blanchard@wias-berlin.de} \scriptsize Weierstrass Institute for Applied Analysis and Stochastics Berlin \and \scriptsize {Thorsten Dickhaus}, % \url{dickhaus@math.hu-berlin.de} \scriptsize Humboldt-University Berlin \and \scriptsize {Niklas Hack}, % \url{niklas.hack@meduniwien.ac.at} \scriptsize Medical University of Vienna \and \scriptsize {Frank Konietschke}, % \url{Frank.Konietschke@medizin.uni-goettingen.de} \scriptsize Georg-August-University G\"ottingen \and \scriptsize {Kornelius Rohmeyer}, % \url{rohmeyer@biostat.uni-hannover.de} \scriptsize Leibniz University Hannover \and \scriptsize {Jonathan Rosenblatt}, % \url{john.ros@gmail.com} \scriptsize Tel Aviv University \and \scriptsize {Marsel Scheer}, % \url{mscheer@ddz.uni-duesseldorf.de} \scriptsize German Diabetes Center \and \scriptsize {Wiebke Werft}, % \url{w.werft@Dkfz-Heidelberg.de} \scriptsize German Cancer Research Center } \maketitle \begin{abstract} $\mu$TOSS is an \verb=R= package providing an open source, easy-to-extend platform for multiple hypothesis testing (MHT), one of the most active research fields in statistics over the last 10-15 years. Its first motivation is to establish a common platform and standardization for MHT procedures at large. The $\mu$TOSS software has been designed and written in the framework of a ``Harvest Programme'' call of the PASCAL2 European research network. Basically, it consists of the two R packages \verb=mutoss= and \verb=mutossGUI=. For researchers, it features a convenient unification of interfaces for MHT procedures (including standardized functions to access existing specific MHT \verb=R= packages such as \verb=multtest= and \verb=multcomp=, as well as recent MHT procedures that are not available elsewhere) and helper functions facilitating the setup of benchmark simulations for comparison of competing methods. For end users, a graphical user interface and an online user's guide for finding appropriate methods for a given specification of the multiple testing problem is included. Ongoing maintenance and subsequent extensions will aim at establishing $\mu$TOSS as a state of the art in statistical computing for MHT. \end{abstract} \section{Introduction} The $\mu$TOSS packages allow the user to discover, apply and compare multiple testing procedure and multiple interval estimation procedures. The $\mu$TOSS packages include a corpus of functions implementing and integrating these procedures and a GUI. These are found in the mutoss and mutossGUI packages respectively. \section{$\mu$TOSS Rationale} The rationale behind the $\mu$TOSS packages is two-fold. It is aimed at allowing statisticians to discover, apply and compare standard and custom multiplicity controlling procedures. This is achieved by the mutoss package. It is also aimed at the researcher wishing to Analise new data or reproduce published results. This is accomplished by the mutossGUI package. % \framebox{\begin{minipage}[t]{1\columnwidth}% At the time of release, the package has only undergone basic testing. This being the case, we recommend new data to be analyzed with standard software alongside $\mu$TOSS. This is planned to change in future releases.% \end{minipage}} \section{System Requirements} \subsection{mutoss Package} The package will run on any machine running R with recommended version 2.8 and above. \subsection{mutossGUI package} On top of the mutoss package requirements, Java Run time Environment ver 5 and above is needed. \section{GUI Work flow} Download and install the \textit{mutossGUI} package. The GUI should start automatically. Others wise load it with \texttt{>mutossGUI()} \subsection{Testing of Hypotheses} If you have already a vector of p-values start at step (5). \begin{enumerate} \item Load the raw data (assumed to be a \textit{data.frame} object) using the \textbf{Data} button. \item Specify the model type and explanatory variables using the \textbf{Model} button.\\ For linear contrasts choose \textbf{Single endpoint in k groups}.\\ For applying the same model to many response variables choose \textbf{Multiple (linear) regression.} \item Define model by choosing response and expalnatory variables. \item Define the hypotheses of interest by specifying the contrasts using the \textbf{Hypotheses} button. \item Insert p-values using the \textbf{p-Value} button or calculate them following the previous steps. \item Choose the error type to control using the \textbf{Error Rate} button. \item Use the \textbf{Adjusted p-Values} to calculate the procedure specific adjusted p-values (you will be propted for additional options when necesary) or choose \textbf{Rejeted} to apply the procedure and reject hypotheses. \item Visualize results by choosing the \textbf{Info} option in the \textbf{Adjusted p-Values} or \textbf{Rejected} buttons. \item Save the output as an R object using the \textbf{File->Export MuToss Object to R} option. \end{enumerate} Further analysis is now possible using the compareMutoss functions or other R functionality. \subsection{Interval Estimations} Steps 1-4 are identical to the hypothesis testing work flow. \begin{enumerate} \item Load the raw data (assumed to be a \textit{data.frame} object) using the \textbf{Data} button. \item Specify the model type and explanatory variables using the \textbf{Model} button.\\ For linear contrasts choose \textbf{Single endpoint in k groups}.\\ For applying the same model to many response variables choose \textbf{Multiple (linear) regression.} \item Define model by choosing response and expalnatory variables. \item Define the contrasts of interest by specifying the contrasts using the \textbf{Hypotheses} button. \item Choose the error type to control using the \textbf{Error Rate} button. \item Use the \textbf{Confidence Intervals }to compute confidence intervals on parameters of interest. \item Visualize results by choosing the \textbf{Info} option in the \textbf{Confidence Intervals} button. \item Save the output as an R object using the \textbf{File->Export MuToss Object to R} option. \end{enumerate} Further analysis is now possible using R functionality. \section{Command Line Work Flow} Download and install the \textit{mutoss} package to access the different procedures in the package (note mutossGUI is not needed for this purpose). A list can presented using\\ \texttt{>help(package='mutoss')} To work with these elementary functions, just use them as any other R function. Seee inline help for further details. To use these functions to read and write into Mutoss S4 class objects use the \textit{mutoss.apply( )} function. See the inline help of the function for further details. \section{Glossary} \begin{description} \item [{Hypotheses-Testing-Procedures}] The corpus of procedures for testing multiple statistical hypotheses. \item [{Interval-Estimating-Procedures}] The corpus of procedures for constructing interval estimates for multiple parameters. \item [{p-Value-Procedures}] The corpus of (multiple) hypotheses testing procedures which rely on the marginal p-values of each hypothesis (and do not require the original data and model). This procedures might possibly require additional information such as logical relations between procedures, a qualitative description of the probabilistic relation between test statistics etc. \item [{Data-Procedures}] The corpus of (multiple) testing procedures which require the original response variables, the explanatory variables (model) and the parameters of interest (contrasts). \\ These procedures can be seen as p-value-procedures with a specific relation between test-statistics which is derived from the model and the contrasts. \item [{Error-Type}] The type of error a procedure aims to control. This can be a hypothesis testing error rate (FWER. FDR,...) or an interval estimation error rate (simultanous coverage, false coverage rate,...). \item [{Error-Rate}] The allowed rate of the \textit{Error Type}. \end{description} \end{document} mutoss/vignettes/simToolManual.actuallyRnw0000644000176200001440000004752515123457163020653 0ustar liggesusers \documentclass[a4paper]{article} \usepackage[OT1]{fontenc} \usepackage{Sweave} \usepackage{amssymb} %\usepackage{hyperref} \newcommand{\sT}{simTool} \newcommand{\pv}{$p$-value } \newcommand{\pvs}{$p$-values } \newcommand{\mts}{MutossSimObject} \newcommand{\tw}[1]{\texttt{#1}} \begin{document} % \VignetteIndexEntry{MuToss Simulation Tool Guide} \title{\sT} \author{MarselScheer} \maketitle \tableofcontents <>= require(mutoss) @ \section{Note} This SweaveFile is part of $\mu$Toss package. But because some parts of this document are computational intensiv the extension of the Sweavefile is ".actuallyRnw". \section{Introduction} \label{introduction} This document will give an introduction to the use of \sT. We will start with a very simple application then raise the degree of complexity in a few steps and in the end reproduce some of the results from Benjamini, Krieger, Yekutieli (2006). Basically for a simulation we need the following things \begin{enumerate} \item a function that generates the data (for example \pvs) \item some procedures that evaluates the generated data (for example the bonferroni correction) \item statistics we want to calculate. For example the the false discovery proportion (FDP). \end{enumerate} If we are speak for example of $1000$ replications, we mean that these 3 steps were repeated $1000$ times. That means, after $1000$ replications there have been $1000$ data sets generated. Every procedure was applied to these $1000$ data sets. If we have specified for example $3$ procedures then every of these procedures will give us an output, this means $3000$ "output objects". And every specified statistic is applied to these $3000$ "output objects". \subsection{Motivation} \subsubsection{Example 1} Suppose we want to compare some characteristics of the two procedures \tw{BH} and \tw{holm}. Denote by $V_n(BH)$ and $V_n(holm)$ the number of true hypotheses rejected. For example we are interested in the distribution of $V_n(BH)$ and $V_n(holm)$ and also in $E[V_n(BH)]$ and $E[V_n(holm)]$. Lets have a look on the arguments of theses procedures: <<>>= args(BH) args(holm) @ Both have the argument \tw{alpha}, which stands for the niveau of the corresponding error measure. \tw{BH} controls the false discovery rate (FDR) and \tw{holm} controls the family-wise error rate (FWER). One question may be how must \tw{alpha} be set in \tw{holm} such that for fixed \tw{alpha} = 0.1 in \tw{BH} we have $E[V_n(BH)] \approx E[V_n(holm)]$? And perhaps we are also interested in how a dependence structure affects the distribution of $V_n(BH)$ and $V_n(holm)$. \subsubsection{Example 2} Suppose you have developed a new procedure controlling some error measure and you want to compare this new procedure with some other already established procedures. Then a simulation study will consist of creating data and gathering statistics for different sample sizes, dependence structures and parameter constellations. $$ $$ Basically it is always the same story. Data is generated by a specific function, perhaps this depends on some parameters, and we want to apply different procedures, again depending on some paramters, to this generated data. Nearly everyone will say, "don't bother me with the details of programming, just do the simulation with the above information and give me a nice \tw{data.frame} which I can analyze." And this is exactly the purpose of the simTool. \subsection{Data generating function} \label{DataGenFun} For now we only want to use the procedures \texttt{BH} and \texttt{holm}. Again, lets have a look on the arguments of these procedures: <<>>= args(BH) args(holm) @ Not much has to be specified. The parameter \texttt{alpha} is the level at which the corresponding error rate should be controlled and \tw{pValues} are the \pvs of the hypotheses that should be tested. In general, if we say that we reject a \pv this means that we reject the corresponding hypotheses. The following function generates data and will be used throughout the whole document. The data generating function \textbf{must have} an entry \$procInput. All in \$procInput will be used as input for the specified procedures. In our situation we will generate a list with 2 entries. \$procInput will only consist of the generated \pvs. And \$groundTruth indicates which \pv corresponds to a true or false hypothesis. <<>>= pValFromEquiCorrData <- function(sampleSize, rho, mu, pi0) { nmbOfFalseHyp <- round(sampleSize * (1-pi0)) nmbOfTrueHyp <- sampleSize - nmbOfFalseHyp muVec <- c(rep(mu, nmbOfFalseHyp), rep(0, nmbOfTrueHyp)) Y <- sqrt(rho) * rnorm(1) + sqrt(1-rho) * rnorm(sampleSize) + muVec return(list( procInput=list(pValues = 1 - pnorm(Y)), groundTruth = muVec == 0 ) ) } @ \subsubsection{Illustration of generated Data} We will now generate 1000 $p$-Values independently (\tw{rho} = 0). \tw{sampleSize * (1-pi0)} = 700 of them correspond to false hypotheses and 300 to true hypotheses. <<>>= set.seed(123) data <- pValFromEquiCorrData( sampleSize = 1000, rho = 0, mu = 2, pi0 = 0.3) @ Lets visualize the different \pvs. <>= local({ pValues = data$procInput$pValues groundTruth = data$groundTruth plot(ecdf(pValues), do.points=FALSE, verticals=TRUE, main="ecdf's of generated p-values") lines(ecdf(pValues[groundTruth]), do.points=FALSE, verticals=TRUE, col=2) lines(ecdf(pValues[!groundTruth]), do.points=FALSE, verticals=TRUE, col=4) abline(0,1,lty=2) legend("right", legend=c("all", "true", "false"), col=c(1,2,4), lty=1, title="p-values") }) @ \subsection{A very simple example} Lets directly start with the function call and then analysing what happend. Just for now we implement a simple version of the data generating function in section \ref{DataGenFun} and a simple version of the \tw{BH}. <<>>= myGen <- function() { pValFromEquiCorrData(sampleSize = 200, rho = 0, mu = 2, pi0=0.5) } BH.05 = function(pValues) { BH(pValues=pValues, alpha = 0.05, silent=TRUE) } @ <<>>= set.seed(123) sim <- simulation(replications = 10, list(funName="myGen", fun=myGen), list(list(funName="BH.simple", fun=BH.05))) @ The following happend: \begin{enumerate} \item Call \tw{myGen} \item Append generated data set to \tw{sim\$data} \item Call \tw{BH.05} with the generated \pvs \item Add to the results of \tw{BH.05} the parameter constellation used by \tw{myGen} and \tw{BH.05} and also the position of the used data in \tw{sim\$data} \item Append this extended results of \tw{BH.05} to \tw{sim\$results} \item repeat this 9 more times \end{enumerate} Since \tw{replications = 10} in \tw{simulation} the object \tw{sim} consists of: <<>>= names(sim) length(sim$data) length(sim$results) @ The structure of one object in \tw{sim\$data} coincides with the structure of the return of \tw{myGen}: <<>>= names(myGen()) names(sim$data[[1]]) @ Lets have a look at the additional information added by \tw{simulation} to the object returned by \tw{BH.05}. First the entries of the pure return value of \tw{BH.05} and then of \tw{sim\$results} <<>>= names(BH.05(runif(100))) names(sim$results[[1]]) @ \tw{data.set.number} is the number of the used data set in \tw{sim\$data}. Since every generated data set is used only once we have <<>>= sapply(sim$results, function(x) x$data.set.number) @ Thus it is possible to reproduce any result directly. Let us reproduce the results of the 6th replication <<>>= idx <- sim$results[[6]]$data.set.number pValues <- sim$data[[idx]]$procInput$pValues all(BH.05(pValues)$adjPValues == sim$results[[6]]$adjPValues) @ Since our data generating function and procedure did not really have any parameter, there is not much information in: <<>>= sim$results[[1]]$parameters @ Note, \tw{BH.05} technically has the parameter \tw{pValues} but since this parameter is the "input channel" for the output data of the data generating function it is not really regarded as parameter that affect the behaviour of the procedure like for example the level $\alpha$ for the error measure. \subsection{A simple example} \label{a.simple.example} Indeed, for the simple example above the simTool is not very helpful. Let us increase the complexity of our simulation. As a first step, we want to increase the parameter \tw{rho} of the data generating function gradually and investigate $E V_n(BH)$ and $E V^2_n(BH)$. <<>>= set.seed(123) sim <- simulation(replications = 10, list(funName="pVGen", fun=pValFromEquiCorrData, sampleSize = 100, rho = seq(0,1,by=0.2), mu = 2, pi0 = 0.5), list(list(funName="BH", fun=BH, alpha = c(0.5, 0.25), silent=TRUE))) @ The following happend: \begin{enumerate} \item Call \tw{myGen} with \tw{rho = 0} \item Append generated data set to \tw{sim\$data} \item Call \tw{BH} with \tw{alpha = 0.5} and the generated \pvs \item Add to the results the parameter constellation used by \tw{myGen} and \tw{BH} and also the position of the used data in \tw{sim\$data} \item Append this extended results to \tw{sim\$results} \item Call \tw{BH} with \tw{alpha = 0.25} and the \textbf{same} \pvs as in 3. \item Add to the results the parameter constellation used by \tw{myGen} and \tw{BH} and also the position of the used data in \tw{sim\$data} \item Append this extended results of to \tw{sim\$results} \item repeat this 9 more times \item Call \tw{myGen} with \tw{rho = 0.2} \item and so on \end{enumerate} So we have now a bunch of data sets and results. <<>>= length(sim$data) length(sim$results) @ 60 data sets have been generated because we have \tw{rho = 0, 0.2, 0.4, 0.6, 0.8, 1} and for each \tw{rho} we generated 10 data sets. We have applied \tw{BH} with \tw{alpha = 0.5} to all 60 data sets and \tw{BH} with \tw{alpha = 0.25}, yielding 120 objects in \tw{results}. We see from the \tw{data.set.number} that after generating a data set it is used two times in series <<>>= sapply(sim$results, function(x) x$data.set.number) @ In order to gather how many true hypotheses have been rejected corresponding to the different parameter constellations we need a function that is able to calculate it. <<>>= NumberOfType1Error <- function(data, result) sum(data$groundTruth * result$rejected) V2 <- function(data, result) NumberOfType1Error(data,result)^2 #result.all <- gatherStatistics(sim, list(NumOfType1Err = NumberOfType1Error)) @ Lets us calculate for the first result object the number of rejected true hypotheses explicitly. <<>>= idx = sim$results[[1]]$data.set.number data = sim$data[[idx]] NumberOfType1Error(data, sim$results[[1]]) @ This means for the following parameter constellation <<>>= sim$results[[1]]$parameters @ we one time observed <>= NumberOfType1Error(data, sim$results[[1]]) @ rejected true null hypotheses. In order to estimate $E V_n(BH)$ and $E V^2_n(BH)$ we have to search in \tw{sim\$results} for the other 9 results using the same parameter constellation. In order to facilitate this task we provide a function. <<>>= result <- gatherStatistics(sim, list(V = NumberOfType1Error, V2 = V2), mean) result @ As you can see every, parameter constellation has its own row in \tw{\$statisticDF}. By the way, again, we see that \pvs is a parameter of \tw{BH} but since it is contained in \tw{\$procInput} it is not considered as "real parameter".\\ If we are interested in more than one statistics, then we simply provide a list with "average functions" <<>>= result <- gatherStatistics(sim, list(V = NumberOfType1Error, V2 = V2), list(mean = mean, sd = function(vec) round(sd(vec), 1))) result$statisticDF @ Another possibility is to call \tw{gatherStatistics} without and "average function". Then every result get his own row in \tw{\$statisticDF} <<>>= result <- gatherStatistics(sim, list(V = NumberOfType1Error, V2 = V2)) head(result$statisticDF) tail(result$statisticDF) @ \subsection{Plotting a bit} <<>>= if(!is.element("sim.plot", ls())) { sim.plot <- simulation(replications = 1000, list(funName = "pVGen", fun = pValFromEquiCorrData, sampleSize = 100, rho = seq(0, 1, by = 0.2), mu = 0, pi0 = 1 ), list( list(funName = "BH", fun = function(pValues, alpha) BH( pValues, alpha, silent=TRUE), alpha = c(0.5, 0.75) ) ) ) } length(sim.plot$data) length(sim.plot$results) @ The are already different kind of R functions that take data.frames and generate histograms, boxplots and so on from them. For some plot example we will need the lattice package. <<>>= require(lattice) @ First we calculate V for every MutossSim object and then plot a histogram and a boxplot of V, see Figure~\ref{fig:HistV1} (p.~\pageref{fig:HistV1}), Figure~\ref{fig:BWV1} (p.~\pageref{fig:BWV1}). <<>>= result.all <- gatherStatistics( sim.plot, list(V = NumberOfType1Error) ) @ <>= print( histogram(~V | rho, data = subset( result.all$statisticDF, alpha=="0.5"))) @ \begin{figure} \begin{center} <>= <> @ \end{center} \caption{Histogram of the number of rejected true hypotheses for alpha equals 0.5} \label{fig:HistV1} \end{figure} <>= print( bwplot(V ~ rho | alpha, data = subset(result.all$statisticDF))) @ \begin{figure} \begin{center} <>= <> @ \end{center} \caption{Boxplot of the number of rejected true hypotheses} \label{fig:BWV1} \end{figure} Also after the "average process" we again get an data.frame which can be used to generate plots. <<>>= result <- gatherStatistics( sim.plot, list(V = NumberOfType1Error), list(Ml = function(x) mean(x) - 2 * sd(x)/sqrt(length(x)), Mu = function(x) mean(x) + 2 * sd(x)/sqrt(length(x)), M = mean, SD = sd) ) subset(result$statisticDF, alpha=="0.5")[1:3,-1] <>= print( xyplot( V.Ml + V.M + V.Mu ~ rho | alpha, data = result$statisticDF, type = "a", auto.key=list( space="right", points=FALSE, lines=TRUE) ) ) @ \begin{figure} \begin{center} <>= <> @ \end{center} \caption{XYPlot of the mean number of rejected true hypotheses with asymptotic 95\% confidence intervalls (pointwise)} \label{fig:XYPlotV1} \end{figure} \subsection{Memory considerations} Up to now we have kept any information in memory. Somtimes, it is useful not to discard any generated Information. For example, if some objects in results appear odd, they can directly be reproduced and of course with all information availible we can investigate anything like distribution or any statistic we did not consider before the simulation. The price for this liberty is in general an extensive memory usage which of course restrict the size of our simulation. On the other hand if we exactly know that we are only interested in $E V_n$ the mean number of true rejected hypotheses, why should we keep the generated \pvs or the index of the rejected \pvs ? Thus it would be enough that an obejct in \tw{\$results} contains $V_n$ the number of rejected true hypotheses. We will know show an example were only the information needed is kept in memory. The following is basically the "simple example" from section \ref{a.simple.example} The main issue is to save memory thus we will calculate the $V_n$ right after applying the procedure to the generated data set. If done so, we can also discard the generated data! <<>>= V.BH <- function(pValues, groundTruth, alpha) { out <- BH(pValues, alpha, silent=TRUE) V <- sum(out$rejected * groundTruth) return(list(V = V)) } @ As already mentioned anything in \tw{\$procInput} will be used as an input for the procedures. Since our procedure now has a parameter \tw{groundTruth} our data generated function has to be adepted. We just move \tw{groundTruth} in \tw{\$procInput}. <<>>= pValFromEquiCorrData2 <- function(sampleSize, rho, mu, pi0) { nmbOfFalseHyp <- round(sampleSize * (1-pi0)) nmbOfTrueHyp <- sampleSize - nmbOfFalseHyp muVec <- c(rep(mu, nmbOfFalseHyp), rep(0, nmbOfTrueHyp)) Y <- sqrt(rho) * rnorm(1) + sqrt(1-rho) * rnorm(sampleSize) + muVec return(list( procInput=list(pValues = 1 - pnorm(Y), groundTruth = muVec == 0) ) ) } @ Obviously, it is unnecessary to store this generated data, because the number of Type 1 Errors we are interested in will be directly calculated. Setting \tw{discardProcInput = TRUE} in \tw{simulation} removes \tw{\$procInput} from the object generated by the data generating function after \tw{\$procInput} has been used by all specified procedures. In our case theses will be the 2 procedures \tw{V.BH} with \tw{alpha = 0.5} and \tw{alpha = 0.25}. <<>>= set.seed(123) sim <- simulation(replications = 10, list(funName="pVGen2", fun=pValFromEquiCorrData2, sampleSize = 100, rho = seq(0,1,by=0.2), mu = 2, pi0 = 0.5), list(list(funName="V.BH", fun=V.BH, alpha = c(0.5, 0.25))), discardProcInput=TRUE) result <- gatherStatistics(sim, list(V = function(data, results) results$V), mean) result @ Here the corresponding table from section \ref{a.simple.example}. <<>>= set.seed(123) sim.simple <- simulation(replications = 10, list(funName="pVGen", fun=pValFromEquiCorrData, sampleSize = 100, rho = seq(0,1,by=0.2), mu = 2, pi0 = 0.5), list(list(funName="BH", fun=BH, alpha = c(0.5, 0.25), silent=TRUE))) result.simple <- gatherStatistics(sim.simple, list(V = NumberOfType1Error), mean) print(result.simple) @ The same results but the memory usage differ much: <<>>= print(s1 <- object.size(sim)) print(s2 <- object.size(sim.simple)) unclass(s2/s1) @ \section{Reproducing some results from BKY (2006)} Now, we will reproduce figure $1$ from the publication. For this we need to estimate the FDR. This will be done be calculating the FDP for every object in \tw{\$results} and then calculating the empirical mean. In this simulation where we exactly know what we want and thus retain only the necessary information. To be able to calculate the FDP right after applying the procedure we need to know which pValue belongs to a true or false hypothesis. Thus, like in the foregoing section, \tw{\$groundTruth} will be an element of \tw{\$procInput}. <<>>= pValFromEquiCorrData2 <- function(sampleSize, rho, mu, pi0) { nmbOfFalseHyp <- round(sampleSize * (1-pi0)) nmbOfTrueHyp <- sampleSize - nmbOfFalseHyp muVec <- c(rep(mu, nmbOfFalseHyp), rep(0, nmbOfTrueHyp)) Y <- sqrt(rho) * rnorm(1) + sqrt(1-rho) * rnorm(sampleSize) + muVec return(list( procInput=list(pValues = 1 - pnorm(Y), groundTruth = muVec == 0) ) ) } @ We also need a function that calculates the FDP. <<>>= FDP <- function(pValues, groundTruth, proc=c("BH", "M-S-HLF")) { if( proc == "BH" ) out <- BH(pValues, alpha = 0.05, silent = TRUE) else out <- adaptiveSTS(pValues, alpha = 0.05, silent = TRUE) R <- sum(out$rejected) if ( R == 0 ) return(list(FDP = 0)) V <- sum(out$rejected * groundTruth) return(list(FDP = V/R)) } @ Now, the (partial) reproduction of the results can start! <<>>= set.seed(123) date() sim <- simulation(replications = 10000, list(funName="pVGen2", fun=pValFromEquiCorrData2, sampleSize = c(16, 32, 64, 128, 256, 512), rho = c(0,0.1,0.5), mu = 5, pi0 = c(0.25, 0.75)), list(list(funName="FDP", fun=FDP, proc=c("BH", "M-S-HLF"))), discardProcInput=TRUE) date() result <- gatherStatistics(sim, list(FDP = function(data, results) results$FDP), mean) result @ <>= print(xyplot(FDP.mean ~ sampleSize | pi0 * rho, data = result$statisticDF, group=proc, type = "a", auto.key=list(space="right", points=FALSE, lines=TRUE) ) ) @ \end{document} mutoss/vignettes/index.html0000644000176200001440000000235115123457163015621 0ustar liggesusers

The mutoss package and accompanying mutossGUI package are designed to ease the application and comparison of multiple hypothesis testing procedures. The GUI is called with the mutossGUI( )

function. Once invoked, the analysis starts by inserting either p-values or raw data and specifying the type of analysis and error control type wanted. The results can be visualized in the GUI or exported as R objects for further analysis- possibly with the accompanying compareMutoss( ) functions.


More information available in the Quick Start Guide.

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Humboldt-University Berlin \and \scriptsize {Niklas Hack}, % \url{niklas.hack@meduniwien.ac.at} \scriptsize Medical University of Vienna \and \scriptsize {Frank Konietschke}, % \url{Frank.Konietschke@medizin.uni-goettingen.de} \scriptsize Georg-August-University G\"ottingen \and \scriptsize {Kornelius Rohmeyer}, % \url{rohmeyer@biostat.uni-hannover.de} \scriptsize Leibniz University Hannover \and \scriptsize {Jonathan Rosenblatt}, % \url{john.ros@gmail.com} \scriptsize Tel Aviv University \and \scriptsize {Marsel Scheer}, % \url{mscheer@ddz.uni-duesseldorf.de} \scriptsize German Diabetes Center \and \scriptsize {Wiebke Werft}, % \url{w.werft@Dkfz-Heidelberg.de} \scriptsize German Cancer Research Center } \maketitle \begin{abstract} $\mu$TOSS is an \verb=R= package providing an open source, easy-to-extend platform for multiple hypothesis testing (MHT), one of the most active research fields in statistics over the last 10-15 years. Its first motivation is to establish a common platform and standardization for MHT procedures at large. The $\mu$TOSS software has been designed and written in the framework of a ``Harvest Programme'' call of the PASCAL2 European research network. Basically, it consists of the two R packages \verb=mutoss= and \verb=mutossGUI=. For researchers, it features a convenient unification of interfaces for MHT procedures (including standardized functions to access existing specific MHT \verb=R= packages such as \verb=multtest= and \verb=multcomp=, as well as recent MHT procedures that are not available elsewhere) and helper functions facilitating the setup of benchmark simulations for comparison of competing methods. For end users, a graphical user interface and an online user's guide for finding appropriate methods for a given specification of the multiple testing problem is included. Ongoing maintenance and subsequent extensions will aim at establishing $\mu$TOSS as a state of the art in statistical computing for MHT. \end{abstract} \section{Introduction} The $\mu$TOSS packages allow the user to discover, apply and compare multiple testing procedure and multiple interval estimation procedures. The $\mu$TOSS packages include a corpus of functions implementing and integrating these procedures and a GUI. These are found in the mutoss and mutossGUI packages respectively. \section{$\mu$TOSS Rationale} The rationale behind the $\mu$TOSS packages is two-fold. It is aimed at allowing statisticians to discover, apply and compare standard and custom multiplicity controlling procedures. This is achieved by the mutoss package. It is also aimed at the researcher wishing to Analise new data or reproduce published results. This is accomplished by the mutossGUI package. % \framebox{\begin{minipage}[t]{1\columnwidth}% At the time of release, the package has only undergone basic testing. This being the case, we recommend new data to be analyzed with standard software alongside $\mu$TOSS. This is planned to change in future releases.% \end{minipage}} \section{System Requirements} \subsection{mutoss Package} The package will run on any machine running R with recommended version 2.8 and above. \subsection{mutossGUI package} On top of the mutoss package requirements, Java Run time Environment ver 5 and above is needed. \section{GUI Work flow} Download and install the \textit{mutossGUI} package. The GUI should start automatically. Others wise load it with \texttt{>mutossGUI()} \subsection{Testing of Hypotheses} If you have already a vector of p-values start at step (5). \begin{enumerate} \item Load the raw data (assumed to be a \textit{data.frame} object) using the \textbf{Data} button. \item Specify the model type and explanatory variables using the \textbf{Model} button.\\ For linear contrasts choose \textbf{Single endpoint in k groups}.\\ For applying the same model to many response variables choose \textbf{Multiple (linear) regression.} \item Define model by choosing response and expalnatory variables. \item Define the hypotheses of interest by specifying the contrasts using the \textbf{Hypotheses} button. \item Insert p-values using the \textbf{p-Value} button or calculate them following the previous steps. \item Choose the error type to control using the \textbf{Error Rate} button. \item Use the \textbf{Adjusted p-Values} to calculate the procedure specific adjusted p-values (you will be propted for additional options when necesary) or choose \textbf{Rejeted} to apply the procedure and reject hypotheses. \item Visualize results by choosing the \textbf{Info} option in the \textbf{Adjusted p-Values} or \textbf{Rejected} buttons. \item Save the output as an R object using the \textbf{File->Export MuToss Object to R} option. \end{enumerate} Further analysis is now possible using the compareMutoss functions or other R functionality. \subsection{Interval Estimations} Steps 1-4 are identical to the hypothesis testing work flow. \begin{enumerate} \item Load the raw data (assumed to be a \textit{data.frame} object) using the \textbf{Data} button. \item Specify the model type and explanatory variables using the \textbf{Model} button.\\ For linear contrasts choose \textbf{Single endpoint in k groups}.\\ For applying the same model to many response variables choose \textbf{Multiple (linear) regression.} \item Define model by choosing response and expalnatory variables. \item Define the contrasts of interest by specifying the contrasts using the \textbf{Hypotheses} button. \item Choose the error type to control using the \textbf{Error Rate} button. \item Use the \textbf{Confidence Intervals }to compute confidence intervals on parameters of interest. \item Visualize results by choosing the \textbf{Info} option in the \textbf{Confidence Intervals} button. \item Save the output as an R object using the \textbf{File->Export MuToss Object to R} option. \end{enumerate} Further analysis is now possible using R functionality. \section{Command Line Work Flow} Download and install the \textit{mutoss} package to access the different procedures in the package (note mutossGUI is not needed for this purpose). A list can presented using\\ \texttt{>help(package='mutoss')} To work with these elementary functions, just use them as any other R function. Seee inline help for further details. To use these functions to read and write into Mutoss S4 class objects use the \textit{mutoss.apply( )} function. See the inline help of the function for further details. \section{Glossary} \begin{description} \item [{Hypotheses-Testing-Procedures}] The corpus of procedures for testing multiple statistical hypotheses. \item [{Interval-Estimating-Procedures}] The corpus of procedures for constructing interval estimates for multiple parameters. \item [{p-Value-Procedures}] The corpus of (multiple) hypotheses testing procedures which rely on the marginal p-values of each hypothesis (and do not require the original data and model). This procedures might possibly require additional information such as logical relations between procedures, a qualitative description of the probabilistic relation between test statistics etc. \item [{Data-Procedures}] The corpus of (multiple) testing procedures which require the original response variables, the explanatory variables (model) and the parameters of interest (contrasts). \\ These procedures can be seen as p-value-procedures with a specific relation between test-statistics which is derived from the model and the contrasts. \item [{Error-Type}] The type of error a procedure aims to control. This can be a hypothesis testing error rate (FWER. 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\alias{mu.test.class} \title{Test for compatible classes for comparison...} \usage{mu.test.class(classes)} \description{Test for compatible classes for comparison} \details{Internal function of muToss package. Takes list of classes and tests if they are all muToss.} \value{True if all classes are muToss. False otherwise.} \author{MuToss-Coding Team.} \arguments{\item{classes}{A list of class names.}} mutoss/man/multiple.down.Rd0000644000176200001440000000264515123457163015465 0ustar liggesusers\name{multiple.down} \alias{multiple.down} \title{Benjamini-Krieger-Yekutieli (2006) Multi-Stage Step-Down} \usage{multiple.down(pValues, alpha)} \description{A p-value procedure which controls the FDR for independent test statistics.} \details{A non-linear step-down p-value procedure which control the FDR for independent test statistics and enjoys more power then other non-adaptive procedure such as the linear step-up (BH). For the case of non-independent test statistics, non-adaptive procedures such as the linear step-up (BH) or the all-purpose conservative Benjamini-Yekutieli (2001) are recommended.} \value{A list containing: \item{rejected}{A logical vector indicating which hypotheses are rejected} \item{criticalValues}{A numeric vector containing critical values used in the step-up-down test.} \item{adjPValues}{A numeric vector containing adjusted p-values.} \item{pi0}{An estimate of the proportion of true null hypotheses among all hypotheses (pi0=m0/m). } \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function and the level \code{alpha}.}} \author{Jonathan Rosenblatt} \arguments{\item{pValues}{A numeric vector of p-values} \item{alpha}{The FDR error rate to control}} \examples{pvals<- runif(100)^2 alpha<- 0.2 result<- multiple.down(pvals, alpha) result plot(result[['criticalValues']]~pvals) plot(result[['adjPValues']]~pvals) abline(v=alpha)} mutoss/man/pval2locfdr.Rd0000644000176200001440000000220115123457163015066 0ustar liggesusers\name{pval2locfdr} \alias{pval2locfdr} \title{Strimmer et al.'s fdrtool-based local fdr} \usage{pval2locfdr(pValues, cutoff)} \description{The function \code{pval2locfdr} takes a vector of p-values and estimates for each case the local fdr.} \value{A list containing: \item{locfdr}{Numeric vector with local FDR values for each case} \item{rejected}{Logical vector indicating rejection/retention for each hypothesis when a \code{cutoff} is supplied.}} \author{JonathanRosenblatt} \references{Strimmer, K. (2008). fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 24: 1461-1462. \cr Efron B., Tibshirani R., Storey J. D. and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment. Journal of the American Statistical Association 96(456):1151-1160.} \arguments{\item{pValues}{pValues to be used.} \item{cutoff}{The local fdr cutoff for rejection. Hypotheses with \code{local fdr} smaller then \code{cutoff} will be rejected.}} \examples{ if (requireNamespace("fdrtool", quietly = TRUE)) { pvals<- runif(1000)^2 pval2locfdr(pvals) pval2locfdr(pValues=pvals, cutoff=0.4) }} mutoss/man/hommel.Rd0000644000176200001440000000247515123457163014146 0ustar liggesusers\name{hommel} \alias{hommel} \title{Hommel's (1988) step-up-procedure} \usage{hommel(pValues, alpha, silent=FALSE)} \description{Hommel's step-up-procedure.} \details{The method is applied to p-values. It controls the FWER in the strong sense when the hypothesis tests are independent or when they are non-negatively associated. The method is based upon the closure principle and the Simes test.} \value{A list containing: \item{adjPValues}{A numeric vector containing the adjusted pValues} \item{rejected}{A logical vector indicating which hypotheses are rejected} \item{criticalValues}{A numeric vector containing critical values used in the step-up-down test.} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function and the level \code{alpha}.} } \author{HackNiklas} \references{ G. Hommel (1988). A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika 75, pp. 383-386} \arguments{ \item{pValues}{pValues to be used. They need a independent structure.} \item{alpha}{The level at which the FWER should be controlled} \item{silent}{Logical. If true, any output on the console will be suppressed.} } \examples{ pval <- c(runif(50), runif(50, 0, 0.01)) result <- hommel(pval, 0.05) result <- hommel(pval, 0.05, silent = TRUE) } mutoss/man/BL.Rd0000644000176200001440000000403715123457163013156 0ustar liggesusers\name{BL} \alias{BL} \title{Benjamini-Liu (1999) step-down procedure} \usage{BL(pValues, alpha, silent=FALSE)} \description{Benjamini-Liu's step-down procedure is applied to pValues. The procedure controls the FDR if the corresponding test statistics are stochastically independent.} \details{The Benjamini-Liu (BL) step-down procedure neither dominates nor is dominated by the Benjamini-Hochberg (BH) step-up procedure. However, in Benjamini and Liu (1999) a large simulation study concerning the power of the two procedures reveals that the BL step-down procedure is more suitable when the number of hypotheses is small. Moreover, if most hypotheses are far from the null then the BL step-down procedure is more powerful than the BH step-up method. The BL step-down method calculates critical values according to Benjamin and Liu (1999), i.e., c_i = 1 - (1 - min(1, (m*alpha)/(m-i+1)))^(1/(m-i+1)) for i = 1,...,m, where m is the number of hypotheses tested. Then, let k be the smallest i for which P_(i) > c_i and reject the associated hypotheses H_(1),...,H_(k-1).} \value{A list containing: \item{adjPValues}{A numeric vector containing the adjusted pValues.} \item{criticalValues}{A numeric vector containing critical values used in the step-up-down test.} \item{rejected}{A logical vector indicating which hypotheses are rejected.} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function and the level \code{alpha}.}} \author{Werft Wiebke} \references{Bejamini, Y. and Liu, W. (1999). A step-down multiple hypotheses testing procedure that controls the false discovery rate under independence. Journal of Statistical Planning and Inference Vol. 82(1-2): 163-170.} \arguments{\item{pValues}{Numeric vector of p-values} \item{alpha}{The level at which the FDR is to be controlled.} \item{silent}{If true any output on the console will be suppressed.}} \examples{alpha <- 0.05 p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1)) result <- BL(p, alpha) result <- BL(p, alpha, silent=TRUE)} mutoss/man/twosamp.marginal.Rd0000644000176200001440000000253315123457163016143 0ustar liggesusers\name{twosamp.marginal} \alias{twosamp.marginal} \title{Marginal two sample test} \usage{twosamp.marginal(data, model, robust, alternative, psi0, equalvar)} \description{The robust version uses the Wilcoxon-Mann-Whitney test, otherwise a two-sample t-test will be performed.} \details{A vector of classlabels needs to be provided to distinguish the two groups.} \value{\item{pValues}{A numeric vector containing the unadjusted pValues}} \author{MuToss-Coding Team} \references{ Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin 1:80-83. Mann, H. and Whitney, D. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics 18:50-60 Student (1908). The probable error of a mean. Biometrika, 6(1):1-25. } \arguments{ \item{data}{the data set} \item{model}{the result of a call to \code{twosamp.model(classlabel)}} \item{robust}{a logical variable indicating whether a two sample t-test or a Wilcoxon-Mann-Whitney test should be used.} \item{alternative}{a character string specifying the alternative hypothesis, must be one of \code{two.sided}, \code{greater} or \code{less}} \item{psi0}{a numeric that defines the hypothesized null value} \item{equalvar}{a logical variable indicating whether to treat the two variances as being equal} } mutoss/man/ABH_pi0_est.Rd0000644000176200001440000000171715123457163014700 0ustar liggesusers\name{ABH_pi0_est} \alias{ABH_pi0_est} \title{Lowest Slope Line (LSL) method of Hochberg and Benjamini for estimating pi0} \usage{ABH_pi0_est(pValues)} \description{The Lowest Slope Line (LSL) method of Hochberg and Benjamini for estimating pi0 is applied to pValues. This method for estimating pi0 is motivated by the graphical approach proposed by Schweder and Spjotvoll (1982), as developed and presented in Hochberg and Benjamini (1990).} \value{\item{pi0.ABH}{The estimated proportion of true null hypotheses.}} \author{WerftWiebke} \references{Hochberg, Y. and Benjamini, Y. (1990). More powerful procedures for multiple significance testing. Statistics in Medicine 9, 811-818. Schweder, T. and Spjotvoll, E. (1982). Plots of P-values to evaluate many tests simultaneously. Biometrika 69, 3, 493-502.} \arguments{\item{pValues}{The raw p-values for the marginal test problems}} \examples{my.pvals <- c(runif(50), runif(50, 0, 0.01)) result <- ABH_pi0_est(my.pvals)} mutoss/man/aorc.Rd0000644000176200001440000000474515123457163013613 0ustar liggesusers\name{aorc} \alias{aorc} \title{Step-up-down test based on the asymptotically optimal rejection curve.} \usage{aorc(pValues, alpha, startIDX_SUD=length(pValues), betaAdjustment, silent=FALSE)} \description{Performs a step-up-down test on pValues. The critical values are based on the asymptotically optimal rejection curve. To have finite FDR control, an automatic adjustment of the critical values is done (see details for more).} \details{The graph of the function f(t) = t / (t * (1 - alpha) + alpha) is called the asymptotically optimal rejection curve. Denote by finv(t) the inverse of f(t). Using the critical values finv(i/n) for i = 1, ..., n yields asymptotic FDR control. To ensure finite control it is possible to adjust f(t) by a factor. The function calculateBetaAdjustment() calculates a beta such that (1 + beta / n) * f(t) can be used to control the FDR for a given finite sample size. If the parameter betaAdjustment is not provided, calculateBetaAdjustment() will be called automatically.} \value{A list containing: \item{adjPValues}{A numeric vector containing the adjusted pValues} \item{rejected}{A logical vector indicating which hypotheses are rejected} \item{criticalValues}{A numeric vector containing critical values used in the step-up-down test} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error (FDR) controlled by the function and the level \code{alpha}.}} \references{Finner, H., Dickhaus, T. & Roters, M. (2009). On the false discovery rate and an asymptotically optimal rejection curve. The Annals of Statistics 37, 596-618.} \author{MarselScheer} \arguments{\item{pValues}{pValues to be used. They are assumed to be stochastically independent.} \item{alpha}{The level at which the FDR shall be controlled.} \item{startIDX_SUD}{The index at which critical value the step-up-down test starts. Default is length(pValues) and thus the function aorc() by default behaves like an step-up test.} \item{betaAdjustment}{A numeric value to adjust the asymptotically optimal rejection curve for the finite sample case. If betaAdjustment is not set an algorithm will calculate it, but this can be time-consuming.} \item{silent}{If true any output on the console will be suppressed.}} \examples{ \dontrun{ # Takes more than 6 seconds therefor CRAN should not check it: r <- c(runif(10), runif(10, 0, 0.01)) result <- aorc(r, 0.05) result <- aorc(r, 0.05, startIDX_SUD = 1) ## step-down result <- aorc(r, 0.05, startIDX_SUD = length(r)) ## step-up} } mutoss/man/storey_pi0_est.Rd0000644000176200001440000000252215123457163015626 0ustar liggesusers\name{storey_pi0_est} \alias{storey_pi0_est} \title{Storey-Taylor-Siegmund estimation of pi0 (finite sample version)} \usage{storey_pi0_est(pValues, lambda)} \description{The Storey-Taylor-Siegmund procedure for estimating pi0 is applied to pValues. The formula is equivalent to that in Schweder and Spjotvoll (1982), page 497, except the additional '+1' in the nominator that introduces a conservative bias which is proven to be sufficiently large for FDR control in finite families of hypotheses if the estimation is used for adjusting the nominal level of a linear step-up test.} \value{A list containing: \item{pi0}{A numeric number containing the estimated value of pi0} \item{lambda}{A numeric number containing the tuning parameter for the estimation}} \author{MarselScheer} \references{Schweder, T. and Spjotvoll, E. (1982). Plots of P-values to evaluate many tests simultaneously. Biometrika 69, 3, 493-502. Storey, J. D., Taylor, J. E. and Siegmund, D. (2004). Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach. JRSS B 66, 1, 187-205.} \arguments{\item{pValues}{The raw p-values for the marginal test problems} \item{lambda}{A tuning parameter in the interval (0, 1)}} \examples{my.pvals <- c(runif(50), runif(50, 0, 0.01)) result <- storey_pi0_est(my.pvals, 0.5)} mutoss/man/printRejected.Rd0000644000176200001440000000137615123457163015466 0ustar liggesusers\name{printRejected} \alias{printRejected} \title{Internal MuTossProjekt-Function} \usage{printRejected(rejected, pValues=NULL, adjPValues=NULL)} \description{Generates standard output for pValues, rejected and adjustedPValues.} \details{It generates an output on the console with the number of hypotheses (number of pValues) and the number of rejected hypotheses (number of rejected pValues). Further a data.frame is constructed, one column containing the rejected pValues, one the index number of the rejected pValues and if given one column with the corresponding adjusted pValues.} \author{MarselScheer} \arguments{\item{rejected}{logical Vector indicating which pValue is rejected.} \item{pValues}{the used pValues.} \item{adjPValues}{the adjusted pValues.}} mutoss/man/requireLibrary.Rd0000644000176200001440000000061115123457163015654 0ustar liggesusers\name{requireLibrary} \alias{requireLibrary} \title{Tries to load a package.} \usage{requireLibrary(package)} \description{Tries to load a package. If this package does not exist, it will ask the user whether the package should be installed and loaded. If the user negates, we will raise an error via stop.} \value{NULL} \author{MuToss-Coding Team} \arguments{\item{package}{Package to load}} mutoss/man/two.stage.Rd0000644000176200001440000000251315123457163014571 0ustar liggesusers\name{two.stage} \alias{two.stage} \title{A p-value procedure which controls the FDR for independent test statistics.} \usage{two.stage(pValues, alpha)} \description{A p-value procedure which controls the FDR for independent test statistics.} \details{In the Benjamini-Krieger-Yekutieli two-stage procedure the linear step-up procedure is used in stage one to estimate m0 which is re-plugged in a linear step-up. This procedure is more powerful then non-adaptive procedures, while still controlling the FDR. On the other hand, error control is not guaranteed under dependence in which case more conservative procedures should be used (e.g. BH).} \value{A list containing: \item{rejected}{A logical vector indicating which hypotheses are rejected} \item{criticalValues}{A numeric vector containing critical values used in the step-up-down test.} \item{adjPValues}{A numeric vector containing adjusted p-values.} \item{pi0}{An estimate of the proportion of true null hypotheses among all hypotheses (pi0=m0/m). } \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function and the level \code{alpha}.}} \author{JonathanRosenblatt} \arguments{\item{pValues}{A numeric vecor of p-values.} \item{alpha}{The FDR error rate to control.}} \examples{pvals<- runif(100)^2 two.stage(pvals, 0.1)} mutoss/man/twostageBR.Rd0000644000176200001440000000301415123457163014734 0ustar liggesusers\name{twostageBR} \alias{twostageBR} \title{Blanchard-Roquain (2009) 2-stage adaptive step-up...} \usage{twostageBR(pValues, alpha, lambda=1, silent=FALSE)} \description{Blanchard-Roquain (2009) 2-stage adaptive step-up} \details{This is an adaptive linear step-up procedure where the proportion of true nulls is estimated using the Blanchard-Roquain 1-stage procedure with parameter lambda, via the formula estimated pi_0 = ( m - R(alpha,lambda) + 1) / ( m*( 1 - lambda * alpha ) ) where R(alpha,lambda) is the number of hypotheses rejected by the BR 1-stage procedure, alpha is the level at which FDR should be controlled and lambda an arbitrary parameter belonging to (0, 1/alpha) with default value 1. This procedure controls FDR at the desired level when the p-values are independent.} \value{A list containing: \item{rejected}{A logical vector indicating which hypotheses are rejected} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function and the level \code{alpha}.}} \author{GillesBlanchard} \references{Blanchard, G. and Roquain, E. (2009) Adaptive False Discovery Rate Control under Independence and Dependence Journal of Machine Learning Research 10:2837-2871.} \arguments{\item{pValues}{the used p-values (assumed to be independent)} \item{alpha}{the level at which the FDR should be controlled.} \item{lambda}{parameter of the procedure, should belong to (0, 1/alpha) (lambda=1 default)} \item{silent}{if true any output on the console will be suppressed.}} mutoss/man/fisher22_fast.Rd0000644000176200001440000000117615123457163015323 0ustar liggesusers\name{fisher22_fast} \alias{fisher22_fast} \title{Fisher (2x2) table association analysis for calculating one (marginal) p-value...} \usage{fisher22_fast(obs, epsilon)} \description{Fisher (2x2) table association analysis for calculating one (marginal) p-value} \value{A list containing: \item{nonrand_p}{The non-randomized (conservative) p-value} \item{rand_p}{The randomized (non-conservative) p-value} \item{prob_table}{The conditional probability of the observed table (given the merginals)}} \author{ThorstenDickhaus} \arguments{\item{obs}{The observed (2x2) table} \item{epsilon}{A threshold for comparing real numbers to zero}} mutoss/man/indepBR.Rd0000644000176200001440000000261115123457163014200 0ustar liggesusers\name{indepBR} \alias{indepBR} \title{Blanchard-Roquain (2009) 1-stage adaptive step-up} \usage{indepBR(pValues, alpha, lambda=1, silent=FALSE)} \description{Blanchard-Roquain (2009) 1-stage adaptive step-up} \details{This is a step-up procedure with critical values C_i = alpha * min( i * ( 1 - lambda * alpha) / (m - i + 1) , lambda ) where alpha is the level at which FDR should be controlled and lambda an arbitrary parameter belonging to (0, 1/alpha) with default value 1. This procedure controls FDR at the desired level when the p-values are independent.} \value{A list containing: \item{rejected}{A logical vector indicating which hypotheses are rejected} \item{criticalValues}{A numeric vector containing critical values used in the step-up-down test} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function and the level \code{alpha}.}} \author{GillesBlanchard} \references{Blanchard, G. and Roquain, E. (2009) Adaptive False Discovery Rate Control under Independence and Dependence Journal of Machine Learning Research 10:2837-2871.} \arguments{\item{pValues}{the used p-values (assumed to be independent)} \item{alpha}{the level at which the FDR should be controlled.} \item{lambda}{parameter of the procedure, should belong to (0, 1/alpha) (lambda=1 default)} \item{silent}{if true any output on the console will be suppressed.}} mutoss/man/calculateBetaAdjustment.Rd0000644000176200001440000000333415123457163017450 0ustar liggesusers\name{calculateBetaAdjustment} \alias{calculateBetaAdjustment} \title{Calculating the beta adjustment factor for the asymptotically optimal rejection curve.} \usage{calculateBetaAdjustment(n, startIDX_SUD, alpha, silent=FALSE, initialBeta=1, maxBinarySteps=50, tolerance=1e-04)} \description{Calculates the beta to adjust the asymptotically optimal rejection curve used by the function aorc() for a finite sample size. Then aorc(..., betaAdjustment = beta) controls the FDR also in the finite sample situation.} \details{The asymptotically optimal rejection curve, denoted by f(t), does not provide finite control of the FDR. calculateBetaAdjustment() calculates a factor, denoted by beta, such that (1 + beta/n) * f(t) provides finite control of the FDR. The beta is calculated with the bisection approach. Assume there are beta1 and beta2 such that the choosing beta1 controls the FDR and beta2 not, then the optimal beta lies in [beta2, beta1]. If the choice (beta2 + beta1)/2 controls the FDR, the optimal FDR lies in [(beta2 + beta1)/2, beta1] and so on.} \value{The adjustment factor that is needed to ensure control of the FDR with the adjusted asymptotically optimal rejection curve at the specified level and sample size.} \author{MarselScheer} \arguments{\item{n}{Number of tests for which the adjusted beta should be calculated.} \item{startIDX_SUD}{Starting index of the step-up-down procedure} \item{alpha}{The level at which the FDR shall be controlled.} \item{silent}{If true any output on the console will be suppressed.} \item{initialBeta}{Initial beta.} \item{maxBinarySteps}{Maximum number of steps that will be performed.} \item{tolerance}{The tolerance to search for an upper FDR bound element in [alpha - tolerance, alpha]}} mutoss/man/ranktruncated.Rd0000644000176200001440000000316715123457163015531 0ustar liggesusers\name{ranktruncated} \alias{ranktruncated} \title{Rank truncated p-Value procedure...} \usage{ranktruncated(pValues, K, silent=FALSE)} \description{Rank truncated p-Value procedure The program computes the exact distribution and with it the p-Value} \details{This function computes the exact distribution of the product of at most K significant p-values of \eqn{L>K} observed p-values. Thus, one gets the pvalue from the exact distribution. This has certain advantages for genomewide association scans: K can be chosen on the basis of a hypothesised disease model, and is independent of sample size. Furthermore, the alternative hypothesis corresponds more closely to the experimental situation where all loci have fixed effects. Please note that this method is implemented with factorials and binomial coefficients and the computation becomes numerical instable for large number of p-values.} \value{Used.pValue: List information about the used pValues; RTP: Test statistic and pValue} \author{Frank Konietschke} \references{Dubridge, F., Koeleman, B.P.C. (2003). Rank truncated product of P-values, with application to genomewide association scans. Genet Epidemiol. 2003 Dec;25(4):360-6} \arguments{\item{pValues}{Vector of p-Values (not sorted)} \item{K}{the number of hypotheses / p-Values being in w} \item{silent}{If true any output on the console will be suppressed.}} \examples{pvalues<-runif(10) result <- ranktruncated(pvalues,K=2,silent=FALSE) # take the K=2 smallest pvalues result <- ranktruncated(pvalues,K=2,silent=TRUE) # take the K=2 smallest pvalues result <- ranktruncated(pvalues,K=5,silent=TRUE) # take the K=5 smallest pvalues} mutoss/man/BY.Rd0000644000176200001440000000325415123457163013173 0ustar liggesusers\name{BY} \alias{BY} \title{Benjamini-Yekutieli (2001) step-up procedure} \usage{BY(pValues, alpha, silent=FALSE)} \description{The Benjamini-Yekutieli step-up procedure is applied to pValues. The procedure ensures FDR control for any dependency structure.} \details{The critical values of the Benjamini-Yekutieli (BY) procedure are calculated by replacing the alpha of the Benjamini-Hochberg procedure by alpha/sum(1/1:m)), i.e., c(i)=i*alpha/(m*(sum(1/1:m))) for i=1,...,m. For large number m of hypotheses the critical values of the BY procedure and the BH procedure differ by a factor log(m). Benjamini and Yekutieli (2001) showed that this step-up procedure controls the FDR at level alpha*m/m0 for any dependency structure among the test statistics.} \value{A list containing: \item{adjPValues}{A numeric vector containing the adjusted pValues} \item{criticalValues}{A numeric vector containing critical values used in the step-up-down test} \item{rejected}{A logical vector indicating which hypotheses are rejected} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function and the level \code{alpha}.}} \author{WerftWiebke} \references{Benjamini, Y. and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29(4):1165-1188.} \arguments{\item{pValues}{The used unadjusted pValues.} \item{alpha}{The level at which the FDR shall be controlled.} \item{silent}{If true any output on the console will be suppressed.}} \examples{alpha <- 0.05 p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1)) result <- BY(p, alpha) result <- BY(p, alpha, silent=TRUE)} mutoss/man/pValuesPlot.Rd0000644000176200001440000000036315123457163015135 0ustar liggesusers\name{pValuesPlot} \alias{pValuesPlot} \title{A function plotting p-values...} \usage{pValuesPlot(pValues)} \description{A function plotting p-values} \author{MarselScheer} \arguments{\item{pValues}{A numeric containing the pValues to plot.}} mutoss/man/mutoss.apply.Rd0000644000176200001440000000264115123457163015336 0ustar liggesusers\name{mutoss.apply} \alias{mutoss.apply} \title{Applies a function to a Mutoss object.} \usage{mutoss.apply(mutossObj, f, label = deparse(substitute(f)) , recordHistory = TRUE , ...)} \description{Applies a function to a Mutoss object.} \details{This functions is intended for applying functions for multiplicity control on Mutoss class objects using the console and not the Mutoss GUI.} \value{A Mutoss object after applying the given function \item{mutossObj}{Object of S4 class Mutoss}} \author{Kornelius Rohmeyer} \arguments{\item{mutossObj}{the Mutoss object the function should be applied to} \item{f}{the function that should be applied} \item{label}{the label affixed to all slots of the Mutoss object that are changed by the procedure, defaults to the name of parameter f} \item{recordHistory}{if true, the calling command is concatenated verbatim to the commandHistory slot} \item{...}{additional parameters that are passed to the function}} \examples{newObjectBonf <- mutoss.apply(new(Class="Mutoss", pValues=runif(10)), f=bonferroni, label="Bonferroni Correction", alpha=0.05) \dontrun{ TODO: EXAMPLE PROBLEM newObjectHolm <- mutoss.apply(new(Class="Mutoss", pValues=runif(10)), f=holm, label="Holm's step-down-procedure", alpha=0.05, silent=T) newObjectAORC <- mutoss.apply(new(Class="Mutoss", pValues=runif(10)), f=aorc, label="Asymptotically optimal rejection curve", alpha=0.05, startIDX_SUD = 1, silent=T) }} mutoss/man/fisher23_fast.Rd0000644000176200001440000000121015123457163015311 0ustar liggesusers\name{fisher23_fast} \alias{fisher23_fast} \title{Fisher-type (2x3) table association analysis for calculating one (marginal) p-value...} \usage{fisher23_fast(obs, epsilon)} \description{Fisher-type (2x3) table association analysis for calculating one (marginal) p-value} \value{A list containing: \item{nonrand_p}{The non-randomized (conservative) p-value} \item{rand_p}{The randomized (non-conservative) p-value} \item{prob_table}{The conditional probability of the observed table (given the merginals)}} \author{ThorstenDickhaus} \arguments{\item{obs}{The observed (2x3) table} \item{epsilon}{A threshold for comparing real numbers to zero}} mutoss/man/reject.Rd0000644000176200001440000000061115123457163014127 0ustar liggesusers\name{reject} \alias{reject} \title{reject} \usage{reject(sorted, criticals)} \description{ Returns the highest rejected p-value and its index given some critical values. } \arguments{ \item{sorted}{ Sorted p-values } \item{criticals}{ Critical values } } \value{ A list with elements \item{cutoff}{highest rejected p-value} \item{cut.index }{index of highest rejected p-value} }mutoss/man/hochberg.Rd0000644000176200001440000000366115123457163014444 0ustar liggesusers\name{hochberg} \alias{hochberg} \title{Hochberg (1988) step-up procedure} \usage{hochberg(pValues, alpha, silent=FALSE)} \description{The Hochberg step-up procedure is based on marginal p-values. It controls the FWER in the strong sense under joint null distributions of the test statistics that satisfy Simes' inequality.} \details{The Hochberg procedure is more powerful than Holm's (1979) procedure, but the test statistics need to be independent or have a distribution with multivariate total positivity of order two or a scale mixture thereof for its validity (Sarkar, 1998). Both procedures use the same set of critical values c(i)=alpha/(m-i+1). Whereas Holm's procedure is a step-down version of the Bonferroni test, and Hochberg's is a step-up version of the Bonferroni test. Note that Holm's method is based on the Bonferroni inequality and is valid regardless of the joint distribution of the test statistics.} \value{A list containing: \item{adjPValues}{A numeric vector containing the adjusted pValues} \item{rejected}{A logical vector indicating which hypotheses are rejected} \item{criticalValues}{A numeric vector containing critical values used in the step-up-down test} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function and the level \code{alpha}.}} \author{WerftWiebke} \references{Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75:800-802.\eqn{n} Huang, Y. and Hsu, J. (2007). Hochberg's step-up method: cutting corners off Holm's step-down method. Biometrika, 94(4):965-975.} \arguments{\item{pValues}{The used raw pValues.} \item{alpha}{The level at which the FDR shall be controlled.} \item{silent}{If true any output on the console will be suppressed.}} \examples{alpha <- 0.05 p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9,max=1)) result <- hochberg(p, alpha) result <- hochberg(p, alpha, silent=TRUE)} mutoss/man/jointCDF.orderedUnif.Rd0000644000176200001440000000225515123457163016566 0ustar liggesusers\name{jointCDF.orderedUnif} \alias{jointCDF.orderedUnif} \title{Joint cumulative distribution function of order statistics of n iid. U(0,1)-distributed random variables} \usage{jointCDF.orderedUnif(vec)} \description{Calculates the joint cumulative distribution function of order statistics of n iid. U(0,1)-distributed random variables at argument vec. Because of numerical issues n should not be greater than 100.} \details{Following Shorack, Wellner (1986) or Finner, Roters (2002) by applying Bolshev's recursion the joint distribution is calculated.} \value{The return value is the following probability P(U_(1:n) <= vec[1], ..., U_(n:n) <= vec[n]), where U_1, ..., U_n are assumed to be iid. uniformly distributed on [0,1]. The i-th ordered value is denoted by U_(i:n) and n equals length(vec)} \references{Shorack, G. R. and Wellner, J. A. (1986). Empirical Processes with Applications to Statistics. Wiley, New York. Finner, H. and Roters, M. (2002). Multiple hypotheses testing and expected type I errors. Ann. Statist. 30, 220-238.} \author{MarselScheer} \arguments{\item{vec}{a numeric vector. The length of the vector also determines the number of random variables considered.}} mutoss/man/multiple.down.adjust.Rd0000644000176200001440000000051215123457163016745 0ustar liggesusers\name{multiple.down.adjust} \alias{multiple.down.adjust} \title{A service function used by multiple...} \usage{multiple.down.adjust(sorted, m)} \description{A service function used by \code{multiple.down}} \author{JonathanRosenblatt} \arguments{ \item{sorted}{A sorted pvalue vector.} \item{m}{The number of hypotheses tested.} } mutoss/man/fisher23.marginal.Rd0000644000176200001440000000104315123457163016071 0ustar liggesusers\name{fisher23.marginal} \alias{fisher23.marginal} \title{Fisher-type (2x3) table association analysis for calculating all marginal p-values...} \usage{fisher23.marginal(data, model)} \description{Fisher-type (2x3) table association analysis for calculating all marginal p-values} \value{A list containing the m marginal p-values} \author{ThorstenDickhaus} \arguments{\item{data}{A tensor of dimension (2x3xm) where m is the number of endpoints (genes, etc.)} \item{model}{A model object indicating that this type of analysis shall be performed}} mutoss/man/simulation.Rd0000644000176200001440000000610015123457163015036 0ustar liggesusers\name{simulation} \alias{simulation} \title{Simulation studies} \usage{simulation(replications, DataGen, listOfProcedures, discardProcInput=FALSE)} \description{This function generates data according to a specified function and parameters and then applies specified procedures to the generated data. The generated data is stored in $data and the results are stored in $results. In order to recognize which results and generated data belong together every result contains an element $data.set.number. The data generating function must return a list which contains a list $procInput. Every element in $procInput will be used as an input parameter for the procedures provided by listOfProcedures.} \value{A list with 2 elements. $data contains the objects generated by DataGen. $results contains the objects generated by the procedures augmented by the data set number and the parameter constellation.} \author{MarselScheer} \arguments{\item{replications}{The number of replications. This means how many simulation runs will be performed.} \item{DataGen}{A list that contains the function and parameters for generating data which will be analyzed be the procedures in listOfProcedures. It must contain the elements $funName (character) $fun (function)} \item{listOfProcedures}{A list of lists which contains the procedures with their parameters for the simulation.} \item{discardProcInput}{A list of lists which contains the procedures with their parameters}} \examples{# this function generates pValues myGen <- function(n, n0) { list(procInput=list(pValues = c(runif(n-n0, 0, 0.01), runif(n0))), groundTruth = c(rep(FALSE, times=n-n0), rep(TRUE, times=n0))) } sim <- simulation(replications = 10, list(funName="myGen", fun=myGen, n=200, n0=c(50,100)), list(list(funName="BH", fun=function(pValues, alpha) BH(pValues, alpha, silent=TRUE), alpha=c(0.25, 0.5)), list(funName="holm", fun=holm, alpha=c(0.25, 0.5),silent=TRUE))) # the following happend: # Call myGen(200,50) and append result to sim$data # Apply bonferroni and holm each with alpha 0.25 and 0.5 to this data set # Append the results to sim$restults # Repeat this 10 times. # Call myGen(200, 100) and append restult to sim$data # Apply bonferroni and holm each with alpha 0.25 and 0.5 to this data set # Append the results to sim$restults # Repeat this 10 times. length(sim$data) length(sim$results) # we now reproduce the 6th item in results print(sim$results[[6]]$data.set.number) print(sim$results[[6]]$parameters) all(BH(sim$data[[2]]$procInput$pValues, 0.5, silent=TRUE)$adjPValues == sim$results[[6]]$adjPValues) # # Just calculating some statistics and making some plots NumberOfType1Error <- function(data, result) sum(data$groundTruth * result$rejected) result.all <- gatherStatistics(sim, list(NumOfType1Err = NumberOfType1Error)) result <- gatherStatistics(sim, list(NumOfType1Err = NumberOfType1Error), list(median=median, mean=mean, sd=sd)) print(result) require(lattice) histogram(~NumOfType1Err | method*alpha, data = result.all$statisticDF) barchart(NumOfType1Err.median + NumOfType1Err.mean ~ method | alpha, data = result$statisticDF)} mutoss/man/mutoss.plotCI.Rd0000644000176200001440000000036315123457163015402 0ustar liggesusers\name{mutoss.plotCI} \alias{mutoss.plotCI} \title{mutoss.plotCI} \usage{mutoss.plotCI(mat)} \description{Plots the confidence intervals} \arguments{\item{mat}{Matrix containing the confidence interval limits. TODO specify the matrix layout.}} mutoss/man/SD.Rd0000644000176200001440000000154515123457163013170 0ustar liggesusers\name{SD} \alias{SD} \title{A general step-down procedure.} \usage{SD(pValues, criticalValues)} \description{A general step-down procedure.} \details{Suppose we have n pValues and they are already sorted. The procedure starts with comparing pValues[1] with criticalValues[1]. If pValues[1] <= criticalValues[1], then the hypothsis associated with pValues[1] is rejected and the algorithm carries on with second smallest pValue and criticalValue in the same way. The algorithm stops rejecting at the first index i for which pValues[i] > criticalValues[i]. Thus pValues[j] is rejected if and only if pValues[i] <= criticalValues[i] for all i <= j.} \value{rejected logical vector indicating if hypotheses are rejected or retained.} \author{MarselScheer} \arguments{\item{pValues}{pValues to be used.} \item{criticalValues}{criticalValues for the step-down procedure}} mutoss/man/sidak.Rd0000644000176200001440000000306415123457163013753 0ustar liggesusers\name{sidak} \alias{sidak} \title{Sidak correction} \usage{sidak(pValues, alpha, silent=FALSE)} \description{The classical Sidak correction returns adjusted p-values, ensuring strong FWER control under the assumption of independence of the input p-values. It only uses the fact that the probability of no incorrect rejection is the product over true nulls of those marginal probabilities (using the assumed independence of p-values).} \details{The procedure is more generally valid for positive orthant dependent test statistics. It is recommended to use the step-down version of the Sidak correction instead (see SidakSD), which is valid under the exact same assumptions but is more powerful.} \value{A list containing: \item{adjPValues}{A numeric vector containing the adjusted pValues} \item{rejected}{(if alpha is given) A logical vector indicating which hypotheses are rejected} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function.}} \references{Sidak, Z. (1967). Rectangular confidence regions for the means of multivariate normal distributions. Journal of the American Statistical Association, 62:626-633.} \author{MuToss-Coding Team} \arguments{ \item{pValues}{pValues to be used.} \item{alpha}{The level at which the FWER shall be controlled (optional).} \item{silent}{logical scalar. If \code{TRUE} no output is generated.} } \examples{alpha <- 0.05 p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1)) result <- sidak(p) result <- sidak(p, alpha) result <- sidak(p, alpha, silent=TRUE)} mutoss/man/Mutoss.Rd0000644000176200001440000000235015123457163014147 0ustar liggesusers\name{Mutoss-class} \docType{class} \alias{Mutoss-class} \title{Class Mutoss} \description{A Mutoss object can store the input for a multiple test procedure and also the output.} \section{Slots}{ \describe{ \item{\code{data}:}{Raw data used in model} \item{\code{model}:}{link function,error family and design} \item{\code{description}:}{a general description} \item{\code{statistic}:}{for Z, T or F statistics} \item{\code{hypotheses}:}{of class ANY} \item{\code{hypNames}:}{identifiers for the hypotheses tested} \item{\code{criticalValues}:}{procedure-specific critical values} \item{\code{pValues}:}{raw p-values} \item{\code{adjPValues}:}{procedure-specific adjusted p-values} \item{\code{errorControl}:}{A Mutoss S4 class of type \code{errorControl}} \item{\code{rejected}:}{Logical vector of the output of a procedure at a given error rate} \item{\code{qValues}:}{Storey's estimates of the supremum of the pFDR} \item{\code{locFDR}:}{Efron's local fdr estimates} \item{\code{pi0}:}{Estimate of the proportion of null hypotheses} \item{\code{confIntervals}:}{Confidence intervals for selected parameters} \item{\code{commandHistory}:}{commandHistory} } } \author{MuToss-Coding Team}mutoss/man/regwq.Rd0000644000176200001440000000520415123457163014003 0ustar liggesusers\name{regwq} \alias{regwq} \title{REGWQ - Ryan / Einot and Gabriel / Welsch test procedure...} \usage{regwq(formula, data, alpha, MSE=NULL, df=NULL, silent=FALSE)} \description{REGWQ - Ryan / Einot and Gabriel / Welsch test procedure This function computes REGWQ test for given data including p samples. It is based on a stepwise or layer approach to significance testing. Sample means are ordered from the smallest to the largest. The largest difference, which involves means that are r = p steps apart, is tested first at \eqn{\alpha} level of significance; if significant, means that are \eqn{r criticalValues[startIDX_SUD], then the procedure retains hypotheses associated with pValues[startIDX_SUD], ..., pValues[n] and carries on in a step-up manner with pValues[startIDX_SUD - 1], ..., pValues[1]. If startIDX_SUD equals n the algorithm behaves like a step-up procedure. If startIDX_SUD equals 1 the algorithm behaves like a step-down procedure.} \value{rejected logical vector indicating if hypotheses are rejected or retained.} \author{MuToss-Coding Team} \arguments{\item{pValues}{pValues to be used.} \item{criticalValues}{criticalValues for the step-up-down procedure} \item{startIDX_SUD}{the index (between 1 and length(pValues)) used for the first comparison of pValues[startIDX_SUD] and criticalValues[startIDX_SUD]. Depending on the result of this comparison the algorithm decides to proceed in step-up or step-down manner.}} mutoss/man/mu.test.same.data.Rd0000644000176200001440000000065715123457163016120 0ustar liggesusers\name{mu.test.same.data} \alias{mu.test.same.data} \title{Tests if the same pvalues were used by different procedures.} \usage{mu.test.same.data(pvals)} \description{Tests if the same pvalues were used by different procedures.} \details{Internal muToss function.} \value{Stops if different data was used by different procedures.} \author{MuToss-Coding Team} \arguments{\item{pvals}{Data frame of p-values used by each procedure.}} mutoss/man/pval2qval.Rd0000644000176200001440000000246715123457163014576 0ustar liggesusers\name{pval2qval} \alias{pval2qval} \title{Strimmer et al.'s fdrtool-based q-values} \usage{pval2qval(pValues, cutoff)} \description{The function \code{pval2qval} takes a vector of p-values and estimates for each case the tail area-based FDR, which can be regarded as a p-value corrected for multiplicity. This is done by calling the \code{fdrtool} function. If a cutoff is supplied, a vector of rejected hypotheses will be returned as well.} \value{A list containing: \item{qValues}{A numeric vector with one q-value for each hypothesis.} \item{rejected}{A logical vector indicating rejection/retention for each hypothesis when \code{cutoff} is supplied.}} \author{JonathanRosenblatt} \references{Strimmer, K. (2008). fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 24: 1461-1462. \cr Storey, J. D. (2003) The Positive False Discovery Rate: A Bayesian Interpretation and the q-Value. The Annals of Statistics 31(6): 2013-2035} \arguments{\item{pValues}{Numeric vector of p-values to be used.} \item{cutoff}{The positive FDR cutoff for rejection. Hypotheses with \code{qValues} smaller then \code{cutoff} will be rejected.}} \examples{ if (requireNamespace("fdrtool", quietly = TRUE)) { pvals<- runif(1000)^2 pval2qval(pvals) pval2qval(pValues=pvals, cutoff=0.1) }} mutoss/man/MutossMethod.Rd0000644000176200001440000000211115123457163015303 0ustar liggesusers\name{MutossMethod-class} \docType{class} \alias{MutossMethod-class} \alias{MutossMethod} \title{Class MutossMethod} \description{A \code{MutossMethod} object describes a method that is applicable to \code{Mutoss} objects.} \section{Slots}{ \describe{ \item{\code{label}:}{A character string that contains the label that will be shown in menus.} \item{\code{errorControl}:}{One of the following character strings: FWER, FWER.weak, FDR, FDX, gFWER, perComparison.} \item{\code{callFunction}:}{A character string that contains the name of the Mutoss-compatible function.} \item{\code{output}:}{A character vector of the \emph{possible} output of the function.} \item{\code{info}:}{A character string with info text. Should contain small description, author, reference etc..} \item{\code{assumptions}:}{A character vector of assumptions for this method.} \item{\code{parameters}:}{A list of optional description of parameters - see MuToss developer handbook.} \item{\code{list}:}{For extensions a list where you can put all your miscellaneous stuff.} } }mutoss/man/snk.Rd0000644000176200001440000000675015123457163013460 0ustar liggesusers\name{snk} \alias{snk} \alias{snk.wrapper} \title{Student - Newman - Keuls rejective test procedure.} \usage{snk(formula, data, alpha, MSE=NULL, df=NULL, silent=FALSE) snk.wrapper(model, data, alpha, silent=FALSE)} \description{Student - Newman - Keuls rejective test procedure. The procedure controls the FWER in the WEAK sense.} \details{This function computes the Student-Newman-Keuls test for given data including p samples. The Newman-Keuls procedure is based on a stepwise or layer approach to significance testing. Sample means are ordered from the smallest to the largest. The largest difference, which involves means that are r = p steps apart, is tested first at \eqn{\alpha} level of significance; if significant, means that are r = p - 1 steps apart are tested at \eqn{\alpha} level of significance and so on. The Newman-Keuls procedure provides an r-mean significance level equal to \eqn{\alpha} for each group of r ordered means, that is, the probability of falsely rejecting the hypothesis that all means in an ordered group are equal to \eqn{\alpha}. It follows that the concept of error rate applies neither on an experimentwise nor on a per comparison basis-the actual error rate falls somewhere between the two. The Newman-Keuls procedure, like Tukey's procedure, requires equal sample n's. However, in this algorithm, the procedure is adapted to unequal sample sized which can lead to still conservative test decisions. It should be noted that the Newman-Keuls and Tukey procedures require the same critical difference for the first comparison that is tested. The Tukey procedure uses this critical difference for all the remaining tests, whereas the Newman-Keuls procedure reduces the size of the critical difference, depending on the number of steps separating the ordered means. As a result, the Newman-Keuls test is more powerful than Tukey's test. Remember, however, that the Newman-Keuls procedure does not control the experimentwise error rate at \eqn{\alpha}.} \value{A list containing: \item{adjPValues}{A numeric vector containing the adjusted pValues} \item{rejected}{A logical vector indicating which hypotheses are rejected} \item{statistics}{A numeric vector containing the test-statistics} \item{confIntervals}{A matrix containing only the estimates} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function.}} \author{Frank Konietschke} \references{Keuls M (1952). "The use of the studentized range in connection with an analysis of variance". Euphytica 1: 112-122} \arguments{\item{formula}{Formula defining the statistical model containing the response and the factor levels.} \item{model}{Model with formula, containing the response and the factor levels} \item{data}{dataset containing the response and the grouping factor.} \item{alpha}{The level at which the error should be controlled. By default it is alpha=0.05.} \item{MSE}{Optional for a given variance of the data.} \item{df}{Optional for a given degree of freedom.} \item{silent}{If true any output on the console will be suppressed.}} \examples{x = rnorm(50) grp = c(rep(1:5,10)) dataframe <- data.frame(x,grp) result <- snk(x~grp, data=dataframe, alpha=0.05,MSE=NULL, df=NULL, silent = TRUE) result <- snk(x~grp, data=dataframe,alpha=0.05,MSE=NULL, df=NULL, silent = FALSE) result <- snk(x~grp, data=dataframe,alpha=0.05,MSE=1, df=Inf, silent = FALSE) # known variance result <- snk(x~grp, data=dataframe,alpha=0.05,MSE=1, df=1000, silent = FALSE) # known variance} mutoss/man/gatherParameters.Rd0000644000176200001440000000246115123457163016156 0ustar liggesusers\name{gatherParameters} \alias{gatherParameters} \title{Extracts the parameters from the simulation()-Object} \usage{gatherParameters(simObject)} \description{Basically, it is a helper function for gatherStatistics(). It extracts the parameters from the simObject$results and creates a data.frame from this. Every used parameter gets its own column. Every row corresponds to one object in simObject$results. If a object A from simObject$results does not contain a parameter P that another object B does, then the row for object A will have "" in the column for the parameter P.} \value{A data.frame with rows for every object in simObject$results and columns for the used parameter.} \author{MarselScheer} \arguments{\item{simObject}{An object returned by simulation()}} \examples{#' # this function generates pValues myGen <- function(n, n0) { list(procInput=list(pValues = c(runif(n-n0, 0, 0.01), runif(n0))), groundTruth = c(rep(FALSE, times=n-n0), rep(TRUE, times=n0))) } # need some simulation()-Object I can work with sim <- simulation(replications = 3, list(funName="myGen", fun=myGen, n=200, n0=c(50,100)), list(list(funName="BH", fun=function(pValues, alpha) BH(pValues, alpha, silent=TRUE), alpha=c(0.25, 0.5)), list(funName="holm", fun=holm, alpha=c(0.25, 0.5),silent=TRUE))) gatherParameters(sim)}mutoss/man/gao.Rd0000644000176200001440000000546215123457163013432 0ustar liggesusers\name{gao} \alias{gao} \alias{gao.wrapper} \title{Xin Gao's non-parametric multiple test procedure is applied to Data.} \usage{gao(formula, data, alpha=0.05, control=NULL, silent=FALSE) gao.wrapper(model, data, alpha, control)} \description{Xin Gao's non-parametric multiple test procedure is applied to Data. The procedure controls the FWER in the strong sense. Here, only the Many-To-One comparisons are computed.} \details{This function computes Xin Gao's nonparametric multiple test procedures in an unbalanced one way layout. It is based upon the following purely nonparametric effects: Let \eqn{F_i} denote the distribution function of sample \eqn{i, i=1,\ldots,a,} and let \eqn{G} denote the mean distribution function of all distribution functions \eqn{(G=1/a\sum_i F_i)}. The effects \eqn{p_i=\int GdF_i} are called unweighted relative effects. If \eqn{p_i>1/2}, the random variables from sample \eqn{i} tend (stochastically) to larger values than any randomly chosen number from the whole experiment. If \eqn{p_i = 1/2}, there is no tendency to smaller nor larger values. However, this approach tests the hypothesis \eqn{H_0^F: F_1=F_j, j=2,\ldots,a} formulated in terms of the distribution functions, simultaneously.} \value{A list containing: \item{adjPValues}{A numeric vector containing the adjusted pValues} \item{rejected}{A logical vector indicating which hypotheses are rejected} \item{confIntervals}{A matrix containing the estimates and the lower and upper confidence bound} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function and the level \code{alpha}.}} \author{Frank Konietschke} \references{Gao, X. et al. (2008). Nonparametric multiple comparison procedures for unbalanced one-way factorial designs. Journal of Statistical Planning and Inference 77, 2574-2591. \eqn{n} The FWER is controlled by using the Hochberg adjustment (Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75, 800-802.)} \arguments{\item{formula}{Formula defining the statistical model, containing the response and the factors} \item{model}{Model with formula, containing the response and the factors} \item{data}{Dataset containing the response and the grouping factor} \item{alpha}{The level at which the FWER shall be controlled. By default it is alpha=0.05.} \item{silent}{If true any output on the console will be suppressed.} \item{control}{The control group for the Many-To-One comparisons. By default it is the first group in lexicographical order.}} \examples{x=c(rnorm(40)) f1=c(rep(1,10),rep(2,10),rep(3,10),rep(4,10)) my.data <- data.frame(x,f1) result <- gao(x~f1,data=my.data, alpha=0.05,control=2, silent=FALSE) result <- gao(x~f1,data=my.data, alpha=0.05,control=2, silent=TRUE) result <- gao(x~f1,data=my.data, alpha=0.05)} mutoss/man/multcomp.wrapper.Rd0000644000176200001440000000575015123457163016203 0ustar liggesusers\name{multcomp.wrapper} \alias{multcomp.wrapper} \title{Simultaneous confidence intervals for arbitrary parametric contrasts in unbalanced one-way layouts.} \usage{multcomp.wrapper(model, hypotheses, alternative, rhs=0, alpha, factorC)} \description{Simultaneous confidence intervals for arbitrary parametric contrasts in unbalanced one-way layouts. The procedure controls the FWER in the strong sense.} \details{this function, it is possible to compute simultaneous confidence for arbitrary parametric contrasts in the unbalanced one way layout. Moreover, it computes p-values. The simultaneous confidence intervals are computed using multivariate t-distribution.} \value{A list containing: \item{adjPValues}{A numeric vector containing the adjusted pValues} \item{rejected}{A logical vector indicating which hypotheses are rejected} \item{confIntervals}{A matrix containing the estimates and the lower and upper confidence bound} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function.}} \author{MuToss-Coding Team} \arguments{\item{model}{a fitted model, for example an object returned by lm, glm, or aov etc. It is assumed that coef and vcov methods are available for model.} \item{hypotheses}{a specification of the linear hypotheses to be tested.} \item{alternative}{a character string specifying the alternative hypothesis, must be one of 'two.sided' (default), 'greater' or 'less'.} \item{rhs}{an optional numeric vector specifying the right hand side of the hypothesis.} \item{alpha}{the significance level} \item{factorC}{character string, specifing the factor variable of interest} } \examples{data(warpbreaks) # Tukey contrast on the levels of the factor 'Tension' multcomp.wrapper(aov(breaks ~ tension, data = warpbreaks), hypotheses = "Tukey", alternative="two.sided", factorC="tension",alpha=0.05) # Williams contrast on 'Tension' multcomp.wrapper(aov(breaks ~ tension, data = warpbreaks), hypotheses= "Williams", alternative="two.sided",alpha=0.05,factorC="tension") # Userdefined contrast matrix K <-matrix(c(-1,0,1,-1,1,0, -1,0.5,0.5),ncol=3,nrow=3,byrow=TRUE) multcomp.wrapper(aov(breaks ~ tension, data = warpbreaks), hypotheses=K, alternative="two.sided",alpha=0.05,factorC="tension") # Two-way anova multcomp.wrapper(aov(breaks ~ tension*wool, data = warpbreaks), hypotheses="Tukey", alternative="two.sided",alpha=0.05,factorC="wool") multcomp.wrapper(aov(breaks ~ tension*wool, data = warpbreaks), hypotheses="Tukey", alternative="two.sided",alpha=0.05,factorC="tension") multcomp.wrapper(aov(breaks ~ tension*wool, data = warpbreaks), hypotheses=K, alternative="two.sided",alpha=0.05, factorC="tension") data(iris) multcomp.wrapper(model=lm(Sepal.Length ~ Species, data=iris), hypotheses="Tukey","two.sided",alpha=0.05, factorC="Species") K <-matrix(c(-1,0,1,-1,1,0, -1,0.5,0.5),ncol=3,nrow=3,byrow=TRUE) multcomp.wrapper(model=lm(Sepal.Length ~ Species, data=iris), hypotheses=K,"two.sided",alpha=0.05, factorC="Species")} mutoss/man/onesamp.marginal.Rd0000644000176200001440000000206215123457163016110 0ustar liggesusers\name{onesamp.marginal} \alias{onesamp.marginal} \title{Marginal one sample test} \usage{onesamp.marginal(data, robust, alternative, psi0)} \description{The robust version uses the Wilcoxon-Mann-Whitney test, otherwise a t-test will be performed.} \value{\item{pValues}{A numeric vector containing the unadjusted pValues}} \author{MuToss-Coding Team} \references{ Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin 1:80-83. Mann, H. and Whitney, D. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics 18:50-60 Student (1908). The probable error of a mean. Biometrika, 6(1):1-25. } \arguments{ \item{data}{the data set} \item{robust}{a logical variable indicating whether a t-test or a Wilcoxon-Mann-Whitney test should be used.} \item{alternative}{a character string specifying the alternative hypothesis, must be one of \code{two.sided}, \code{greater} or \code{less}} \item{psi0}{a numeric that defines the hypothesized null value} } mutoss/man/nparcomp.Rd0000644000176200001440000000757315123457163014510 0ustar liggesusers\name{nparcomp} \alias{nparcomp} \title{Simultaneous confidence intervals for relative contrast effects...} \usage{nparcomp(formula, data, type=c("UserDefined", "Tukey", "Dunnett", "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus", "UmbrellaWilliams"), control=NULL, conflevel=0.95, alternative=c("two.sided", "less", "greater"), rounds=3, correlation=FALSE, asy.method=c("logit", "probit", "normal", "mult.t"), plot.simci=FALSE, info=TRUE, contrastMatrix=NULL)} \description{Simultaneous confidence intervals for relative contrast effects The procedure controls the FWER in the strong sense.} \details{With this function, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes adjusted p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers} \value{A list containing: \item{adjPValues}{A numeric vector containing the adjusted pValues} \item{rejected}{A logical vector indicating which hypotheses are rejected} \item{confIntervals}{A matrix containing the estimates and the lower and upper confidence bound} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function.}} \author{FrankKonietschke} \arguments{\item{formula}{A two-sided 'formula' specifying a numeric response variable and a factor with more than two levels. If the factor contains less than 3 levels, an error message will be returned} \item{data}{data A dataframe containing the variables specified in formula} \item{type}{type Character string defining the type of contrast. It should be one of "Tukey", "Dunnett", "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus"} \item{control}{control Character string defining the control group in Dunnett comparisons. By default it is the first group by lexicographical ordering} \item{conflevel}{The confidence level for the 1 - conflevel confidence intervals. By default it is 0.05} \item{alternative}{Character string defining the alternative hypothesis, one of "two.sided", "less" or "greater"} \item{rounds}{Number of rounds for the numeric values of the output. By default it is rounds=3} \item{correlation}{Correlation A logical whether the estimated correlation matrix and covariance matrix should be printed} \item{asy.method}{asy.method character string defining the asymptotic approximation method, one of "logit", for using the logit transformation function, "probit", for using the probit transformation function, "normal", for using the multivariate normal distribution or "mult.t" for using a multivariate t-distribution with a Satterthwaite Approximation} \item{plot.simci}{plot.simci A logical indicating whether you want a plot of the confidence intervals} \item{info}{info A logical whether you want a brief overview with informations about the output} \item{contrastMatrix}{arbitrary contrast matrix given by the user}} \examples{ \dontrun{# TODO Check this example and set a seed! grp <- rep(1:5,10) x <- rnorm(50, grp) dataframe <- data.frame(x,grp) # Williams Contrast nparcomp(x ~grp, data=dataframe, asy.method = "probit", type = "Williams", alternative = "two.sided", plot.simci = TRUE, info = TRUE) # Dunnett Contrast nparcomp(x ~grp, data=dataframe, asy.method = "probit",control=1, type = "Dunnett", alternative = "two.sided", plot.simci = TRUE, info = TRUE) # Dunnett dose 3 is baseline nparcomp(x ~grp, data=dataframe, asy.method = "probit", type = "Dunnett", control = "3",alternative = "two.sided", plot.simci = TRUE, info = TRUE) } } mutoss/man/mu.test.name.Rd0000644000176200001440000000062715123457163015200 0ustar liggesusers\name{mu.test.name} \alias{mu.test.name} \title{Tests if all hypotheses have the same names.} \usage{mu.test.name(hyp.names)} \description{Tests if all hypotheses have the same names.} \details{Internal muToss function.} \value{Gives a notice when hypotheses names in different procedures are found different.} \author{MuToss-Coding Team} \arguments{\item{hyp.names}{Character vector of hypotheses names.}} mutoss/man/compareMutoss.Rd0000644000176200001440000000747215123457163015530 0ustar liggesusers\name{compareMutoss} \title{Functions for comparing outputs of different procedures.} \usage{compareMutoss(...) mu.compare.adjusted(comparison.list, identify.check=F) mu.compare.critical(comparison.list, identify.check=F) mu.compare.summary(comparison.list)} \description{Functions for comparing outputs of different procedures.} \details{These functions are used to compare the results of different multiple comparisons procedures stored as Mutoss class objects. \code{compareMutoss} takes as input an arbitrary number of Mutoss objects and arranges them in a simple list objects (non S4). \code{mu.compare.adjuted}, \code{mu.compare.critical} and \code{mu.compare.summary} take the output of the \code{compareMutoss} and plots or summerize the results textually or graphically.} \value{\item{compareMutoss}{Returns a list with the following components:\cr \bold{types}: Character vector of error types corrsponding to each procedure.\cr \bold{rates}: Numeric vector of error rates used for each procedure.\cr \bold{pi.nulls}: Numeric vector of estimates of the proportion of true null hypothesis if avilable.\cr \bold{raw.pValues}: The raw p-values used for each procedure.\cr \bold{adjusted.pvals}: Data frame with columns holding procedure specific adjusted p values.\cr \bold{criticalValue}: Data frame with columns holding the critical values corresponding to each procedure and error rate.\cr \bold{rejections}: Data frame with columns holding logical vectors of rejected hypotheses (TRUE for rejected).\cr} \item{mu.compare.adjusted}{Creates a plot with the adjusted p-values for each procedure.} \item{mu.compare.critical}{Creates a plot with the critical values for each procedure and error rate.} \item{mu.compare.summary}{Creates a short textual summary for comparing results of different procedures.}} \author{Jonathan Rosenblatt} \alias{compareMutoss} \alias{mu.compare.adjusted} \alias{mu.compare.critical} \alias{mu.compare.summary} \arguments{\item{...}{An arbitrary number of Motoss class objects.} \item{comparison.list}{The output of the \code{compareMutoss} function.} \item{identify.check}{Logical parameter specifying if hypotheses should be identified on the output plots.} } \examples{# TODO: EXAMPLE PROBLEMS \dontrun{ \dontrun{Creating several Mutoss class objects} mu.test.obj.1 <- mutoss.apply(new(Class="Mutoss", pValues=runif(10)), f=bonferroni, label="Bonferroni Correction", alpha=0.05, silent=T) mu.test.obj.2 <- mutoss.apply(new(Class="Mutoss", pValues=runif(10)), f=holm, label="Holm's step-down-procedure", alpha=0.05, silent=T) mu.test.obj.3 <- mutoss.apply(new(Class="Mutoss", pValues=runif(10)), f=aorc, label="Asymtotically optimal rejection curve", alpha=0.05, startIDX_SUD = 1, silent=T) \dontrun{Trying to coercing a non-Mutoss object} compareMutoss(1) \dontrun{ Coercing several objects into a list} compare.1<- compareMutoss(mu.test.obj.1, mu.test.obj.2) compare.2<- compareMutoss(mu.test.obj.1, mu.test.obj.2, mu.test.obj.3) \dontrun{Plotting the adjusted pvalues. Identification available.} mu.compare.adjusted(compare.1, T) mu.compare.adjusted(compare.2, T) \dontrun{Plotting the critical values. Identification available.} mu.compare.critical(compare.1, T) mu.compare.critical(compare.2, T) \dontrun{Showing a textual sumary} mu.compare.summary(compare.1) mu.compare.summary(compare.2) }} mutoss/man/BH.Rd0000644000176200001440000000474715123457163013162 0ustar liggesusers\name{BH} \alias{BH} \title{Benjamini-Hochberg (1995) linear step-up procedure} \usage{BH(pValues, alpha, silent=FALSE)} \description{Benjamini-Hochbergs Linear Step-Up Procedure. The procedure controls the FDR when the test statistics are stochastically independent or satisfy positive regression dependency (PRDS) (see Benjamini and Yekutieli 2001 for details). The Benjamini-Hochberg (BH) step-up procedure considers ordered pValues P_(i). It defines k as the largest i for which P_(i) <= i*alpha/m and then rejects all associated hypotheses H_(i) for i=1,...,k. In their seminal paper, Benjamini and Hochberg (1995) show that for 0 <= m_0 <= m independent pValues corresponding to true null hypotheses and for any joint distribution of the m_1 = m-m_0 p-values corresponding to the non null hypotheses, the FDR is controlled at level (m_0/m)*alpha. Under the assumption of the PRDS property, (for details see Benjamini and Yekutieli 2001). In Benjamini et al. (2006) the BH procedure is improved by adaptive procedures which use an estimate of m_0 and apply the BH method al level alpha'=alpha*m/m_0, to fully exhaust the desired level alpha (see Adaptive Benjamini Hochberg and Two Stage Banjamini Yekutieli).} \value{A list containing: \item{adjPValues}{A numeric vector containing the adjusted pValues} \item{criticalValues}{A numeric vector containing critical values used in the step-up test} \item{rejected}{A logical vector indicating which hypotheses are rejected} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function and the level \code{alpha}.}} \author{Werft Wiebke} \references{Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to mulitple testing. Journal of the Royal Statistical Society, Series B, 57:289-300.\eqn{n} Benjamini, Y. and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29(4):1165-1188.\eqn{n} Benjamini, Y., Krieger, A. and Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika, 93(3):491-507.} \arguments{\item{pValues}{The used unadjusted pValues.} \item{alpha}{The level at which the FDR shall be controlled.} \item{silent}{If true any output on the console will be suppressed.}} \examples{alpha <- 0.05 p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1)) result <- BH(p, alpha) result <- BH(p, alpha, silent=TRUE)} mutoss/man/TSBKY_pi0_est.Rd0000644000176200001440000000220715123457163015175 0ustar liggesusers\name{TSBKY_pi0_est} \alias{TSBKY_pi0_est} \title{Two-step estimation method of Benjamini, Krieger and Yekutieli for estimating pi0} \usage{TSBKY_pi0_est(pValues, alpha)} \description{The two-step estimation method of Benjamini, Krieger and Yekutieli for estimating pi0 is applied to pValues. It consists of the following two steps: Step 1. Use the linear step-up procedure at level alpha' =alpha/(1+alpha). Let r1 be the number of rejected hypotheses. If r1=0 do not reject any hypothesis and stop; if r1=m reject all m hypotheses and stop; otherwise continue. Step 2. Let \eqn{\hat{m0} =(m - r1)} and \eqn{\hat{pi0} = \hat{m0} / m}.} \value{\item{pi0.TSBKY}{The estimated proportion of true null hypotheses.}} \author{WerftWiebke} \references{Benjamini, Y., Krieger, A. and Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate Biometrika 93, 3, page 495.} \arguments{\item{pValues}{The raw p-values for the marginal test problems} \item{alpha}{The parameter (to be interpreted as significance level) for the procedure}} \examples{my.pvals <- c(runif(50), runif(50, 0, 0.01)) result <- TSBKY_pi0_est(my.pvals, 0.1)} mutoss/man/fisher22.marginal.Rd0000644000176200001440000000103115123457163016065 0ustar liggesusers\name{fisher22.marginal} \alias{fisher22.marginal} \title{Fisher (2x2) table association analysis for calculating all marginal p-values...} \usage{fisher22.marginal(data, model)} \description{Fisher (2x2) table association analysis for calculating all marginal p-values} \value{A list containing the m marginal p-values} \author{ThorstenDickhaus} \arguments{\item{data}{A tensor of dimension (2x2xm) where m is the number of endpoints (genes, etc.)} \item{model}{A model object indicating that this type of analysis shall be performed}} mutoss/man/paired.marginal.Rd0000644000176200001440000000302115123457163015706 0ustar liggesusers\name{paired.marginal} \alias{paired.marginal} \title{Marginal paired two sample test} \usage{paired.marginal(data, model, robust, alternative, psi0, equalvar)} \description{The robust version uses the Wilcoxon signed rank test, otherwise a paired t-test will be performed.} \details{A vector of classlabels needs to be provided to distinguish the two paired groups. The arrangement of group indices does not matter, as long as the columns are arranged in the same corresponding order between groups. For example, if group 1 is code as 0 and group 2 is coded as 1, for 3 pairs of data, it does not matter if the classlabel is coded as (0,0,0,1,1,1) or (1,1,1,0,0,0) or (0,1,0,1,0,1) or (1,0,1,0,1,0), the paired differences between groups will be calculated as group2 - group1.} \value{ \item{pValues}{A numeric vector containing the unadjusted pValues} } \author{MuToss-Coding Team} \references{ Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin 1:80-83. } \arguments{ \item{data}{the data set} \item{model}{the result of a call to \code{paired.model(classlabel)}} \item{robust}{a logical variable indicating whether a paired t-test or a Wilcoxon signed rank test should be used.} \item{alternative}{a character string specifying the alternative hypothesis, must be one of \code{two.sided}, \code{greater} or \code{less}} \item{psi0}{a numeric that defines the hypothesized null value} \item{equalvar}{a logical variable indicating whether to treat the two variances as being equal} } mutoss/man/SidakSD.Rd0000644000176200001440000000260515123457163014142 0ustar liggesusers\name{SidakSD} \alias{SidakSD} \title{Sidak-like (1987) step-down procedure} \usage{SidakSD(pValues, alpha, silent=FALSE)} \description{The Sidak-like (1987) step-down procedure is applied to pValues The Sidak-like step-down procedure is an improvement over the Holm's (1979) step-down procedure. The improvement is analogous to Sidak's correction over the original Bonferroni procedure. This Sidak-like step-down procedure assumes positive orthant dependent test statistics.} \value{A list containing: \item{adjPValues}{a numeric vector containing the adjusted pValues} \item{rejected}{a logical vector indicating which hypotheses are rejected} \item{criticalValues}{a numeric vector containing critical values used in the step-up-down test} \item{errorControl}{A Mutoss S4 class of type \code{errorControl}, containing the type of error controlled by the function and the level \code{alpha}.}} \author{WerftWiebke} \references{Hollander, B.S. and Covenhaver, M.D. (1987). An Improved Sequentially Rejective Bonferroni Test Procedure. Biometrics, 43(2):417-423, 1987.} \arguments{\item{pValues}{The used raw pValues.} \item{alpha}{The level at which the FWER shall be controlled.} \item{silent}{If true any output on the console will be suppressed.}} \examples{alpha <- 0.05 p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9,max=1)) result <- SidakSD(p, alpha) result <- SidakSD(p, alpha, silent=TRUE)} mutoss/man/mu.test.type.Rd0000644000176200001440000000063115123457163015234 0ustar liggesusers\name{mu.test.type} \alias{mu.test.type} \title{Tests that different procedures use the same error types.} \usage{mu.test.type(types)} \description{Tests that different procedures use the same error types.} \details{Internal muToss function.} \value{Returns a notice if error types differ.} \author{MuToss-Coding Team.} \arguments{\item{types}{Character vector of error types extracted from muToss objects.}} mutoss/man/SU.Rd0000644000176200001440000000163015123457163013204 0ustar liggesusers\name{SU} \alias{SU} \title{A general step-up procedure.} \usage{SU(pValues, criticalValues)} \description{A general step-up procedure.} \details{Suppose we have n pValues and they are already sorted. The procedure starts with comparing pValues[n] with criticalValues[n]. If pValues[n] > criticalValues[n], then the hypothesis associated with pValues[n] is retained and the algorithm carries on with the next pValue and criticalValue, here for example pValues[n-1] and criticalValues[n-1]. The algorithm stops retaining at the first index i for which pValues[i] <= criticalValues[i]. Thus pValues[j] is rejected if and only if their exists an index i with j <= i and pValues[i] <= criticalValues[i].} \value{rejected logical vector indicating if hypotheses are rejected or retained.} \author{MarselScheer} \arguments{\item{pValues}{pValues to be used.} \item{criticalValues}{criticalValues for the step-up procedure}} mutoss/DESCRIPTION0000644000176200001440000000360315127645135013325 0ustar liggesusersPackage: mutoss Type: Package Title: Unified Multiple Testing Procedures Version: 0.1-14 Authors@R: c( person(given = c("MuToss", "Coding"), family = "Team", role = "aut", comment = "Berlin 2010"), person(given = "Gilles", family = "Blanchard", role = "aut"), person(given = "Thorsten", family = "Dickhaus", role = "aut"), person(given = "Niklas", family = "Hack", role = "aut"), person(given = "Frank", family = "Konietschke", role = "aut"), person(given = "Kornelius", family = "Rohmeyer", role = c("aut", "cre"), email = "rohmeyer@small-projects.de"), person(given = "Jonathan", family = "Rosenblatt", role = "aut"), person(given = "Marsel", family = "Scheer", role = "aut"), person(given = "Wiebke", family = "Werft", role = "aut") ) Description: Designed to ease the application and comparison of multiple hypothesis testing procedures for FWER, gFWER, FDR and FDX. Methods are standardized and usable by the accompanying 'mutossGUI'. Depends: R (>= 2.10.0), mvtnorm Suggests: fdrtool, qvalue, testthat, lattice Imports: plotrix, multtest (>= 2.2.0), multcomp (>= 1.1-0), methods License: GPL LazyLoad: yes LazyData: true URL: https://github.com/kornl/mutoss/ BugReports: https://github.com/kornl/mutoss/issues/ NeedsCompilation: no Packaged: 2025-12-26 10:08:10 UTC; kornel Author: MuToss Coding Team [aut] (Berlin 2010), Gilles Blanchard [aut], Thorsten Dickhaus [aut], Niklas Hack [aut], Frank Konietschke [aut], Kornelius Rohmeyer [aut, cre], Jonathan Rosenblatt [aut], Marsel Scheer [aut], Wiebke Werft [aut] Maintainer: Kornelius Rohmeyer Repository: CRAN Date/Publication: 2026-01-08 06:10:37 UTC