mt.plot {multtest} | R Documentation

## Plotting results from multiple testing procedures

### 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, yongchao.ge@mssm.edu.

### 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)

[Package multtest version 2.0.0 Index]