| mt.plot {multtest} | R Documentation |
This function produces a number of graphical summaries for the results of multiple testing procedures and their corresponding adjusted p-values.
mt.plot(adjp, teststat, plottype="rvsa", logscale=FALSE, alpha=seq(0, 1, length = 100), proc, leg=c(0, 0), ...)
adjp |
A matrix of adjusted p-values, with rows
corresponding to hypotheses (genes) and columns to multiple testing
procedures. This matrix could be obtained from the functions
|
teststat |
A vector of test statistics for each of the hypotheses. This vector could be obtained from the functions |
plottype |
A character string specifying the type of graphical
summary for the results of the multiple testing procedures. |
logscale |
A logical variable for the |
alpha |
A vector of nominal Type I error rates for the |
proc |
A vector of character strings containing the names of the multiple testing procedures, to be used in the legend. |
... |
Graphical parameters such as |
leg |
A vector of coordinates for the legend. |
Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine,
Yongchao Ge, yongchao.ge@mssm.edu.
S. Dudoit, J. P. Shaffer, and J. C. Boldrick (Submitted). Multiple hypothesis testing in microarray experiments.
Y. Ge, S. Dudoit, and T. P. Speed. Resampling-based multiple testing for microarray data hypothesis, Technical Report \#633 of UCB Stat. http://www.stat.berkeley.edu/~gyc
mt.maxT, mt.minP, mt.rawp2adjp, mt.reject, mt.teststat, golub.
# Gene expression data from Golub et al. (1999)
# To reduce computation time and for illustrative purposes, we condider only
# the first 100 genes and use the default of B=10,000 permutations.
# In general, one would need a much larger number of permutations
# for microarray data.
data(golub)
smallgd<-golub[1:100,]
classlabel<-golub.cl
# Permutation unadjusted p-values and adjusted p-values for maxT procedure
res1<-mt.maxT(smallgd,classlabel)
rawp<-res1$rawp[order(res1$index)]
teststat<-res1$teststat[order(res1$index)]
# Permutation adjusted p-values for simple multiple testing procedures
procs<-c("Bonferroni","Holm","Hochberg","SidakSS","SidakSD","BH","BY")
res2<-mt.rawp2adjp(rawp,procs)
# Plot results from all multiple testing procedures
allp<-cbind(res2$adjp[order(res2$index),],res1$adjp[order(res1$index)])
dimnames(allp)[[2]][9]<-"maxT"
procs<-dimnames(allp)[[2]]
procs[7:9]<-c("maxT","BH","BY")
allp<-allp[,procs]
cols<-c(1:4,"orange","brown","purple",5:6)
ltypes<-c(3,rep(1,6),rep(2,2))
# Ordered adjusted p-values
mt.plot(allp,teststat,plottype="pvsr",proc=procs,leg=c(80,0.4),lty=ltypes,col=cols,lwd=2)
# Adjusted p-values in original data order
mt.plot(allp,teststat,plottype="pvsi",proc=procs,leg=c(80,0.4),lty=ltypes,col=cols,lwd=2)
# Number of rejected hypotheses vs. level of the test
mt.plot(allp,teststat,plottype="rvsa",proc=procs,leg=c(0.05,100),lty=ltypes,col=cols,lwd=2)
# Adjusted p-values vs. test statistics
mt.plot(allp,teststat,plottype="pvst",logscale=TRUE,proc=procs,leg=c(0,4),pch=ltypes,col=cols)