mt.plot: Plotting results from multiple testing procedures

In multtest: Resampling-based multiple hypothesis testing

Description

This function produces a number of graphical summaries for the results of multiple testing procedures and their corresponding adjusted p-values.

Usage

mt.plot(adjp, teststat, plottype="rvsa", logscale=FALSE, alpha=seq(0, 1, length = 100), proc, leg=c(0, 0), ...)

Arguments

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 mt.maxT, mt.minP, or mt.rawp2adjp.
teststat	A vector of test statistics for each of the hypotheses. This vector could be obtained from the functions mt.teststat, mt.maxT, or mt.minP.
plottype	A character string specifying the type of graphical summary for the results of the multiple testing procedures. If plottype="rvsa", the number of rejected hypotheses is plotted against the nominal Type I error rate for each of the procedures given in proc. If plottype="pvsr", the ordered adjusted p-values are plotted for each of the procedures given in proc. This can be viewed as a plot of the Type I error rate against the number of rejected hypotheses. If plottype="pvst", the adjusted p-values are plotted against the test statistics for each of the procedures given in proc. If plottype="pvsi", the adjusted p-values are plotted for each of the procedures given in proc using the original data order.
logscale	A logical variable for the pvst and pvsi plots. If logscale is TRUE, the negative decimal logarithms of the adjusted p-values are plotted against the test statistics or gene indices. If logscale is FALSE, the adjusted p-values are plotted against the test statistics or gene indices.
alpha	A vector of nominal Type I error rates for the rvsa plot.
proc	A vector of character strings containing the names of the multiple testing procedures, to be used in the legend.
...	Graphical parameters such as col, lty, pch, and lwd may also be supplied as arguments to the function (see par).
leg	A vector of coordinates for the legend.

Author(s)

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine,
Yongchao Ge, [e-mail].

References

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

See Also

mt.maxT, mt.minP, mt.rawp2adjp, mt.reject, mt.teststat, golub.

Examples

# 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)

multtest documentation built on Nov. 8, 2020, 11:03 p.m.