mt.rawp2adjp {multtest}  R Documentation 
This function computes adjusted pvalues for simple multiple testing procedures from a vector of raw (unadjusted) pvalues. The procedures include the Bonferroni, Holm (1979), Hochberg (1988), and Sidak procedures for strong control of the familywise Type I error rate (FWER), and the Benjamini & Hochberg (1995) and Benjamini & Yekutieli (2001) procedures for (strong) control of the false discovery rate (FDR).
mt.rawp2adjp(rawp, proc=c("Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BH", "BY"))
rawp 
A vector of raw (unadjusted) pvalues for each
hypothesis under consideration. These could be nominal
pvalues, for example, from ttables, or permutation
pvalues as given in mt.maxT and mt.minP . If the
mt.maxT or mt.minP functions are used, raw
pvalues should be given in the original data order,
rawp[order(index)] . 
proc 
A vector of character strings containing the names of the
multiple testing procedures for which adjusted pvalues are to
be computed. This vector should include any of the following:
"Bonferroni" , "Holm" , "Hochberg" ,
"SidakSS" , "SidakSD" , "BH" , "BY" .

Adjusted pvalues are computed for simple FWER and FDR controlling procedures based on a vector of raw (unadjusted) pvalues.
A list with components
adjp 
A matrix of adjusted pvalues, with rows corresponding to hypotheses and columns to multiple testing procedures. Hypotheses are sorted in increasing order of their raw (unadjusted) pvalues. 
index 
A vector of row indices, between 1 and
length(rawp) , where rows are sorted according to
their raw (unadjusted) pvalues. To obtain the adjusted
pvalues in the original data order, use
adjp[order(index),] .

Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine,
Yongchao Ge, yongchao.ge@mssm.edu.
Y. Benjamini and Y. Hochberg (1995). Controlling the false discovery
rate: a practical and powerful approach to multiple
testing. J. R. Statist. Soc. B. Vol. 57: 289300.
Y. Benjamini and D. Yekutieli (2001). The control of the false discovery
rate in multiple hypothesis testing under dependency. Annals of
Statistics. Accepted.
S. Dudoit, J. P. Shaffer, and J. C. Boldrick (Submitted). Multiple
hypothesis testing in microarray experiments.
Y. Ge, S. Dudoit, and T. P. Speed. Resamplingbased multiple testing for microarray data hypothesis, Technical Report #633 of UCB Stat. http://www.stat.berkeley.edu/~gyc
Y. Hochberg (1988). A sharper Bonferroni procedure for multiple tests of
significance, Biometrika. Vol. 75: 800802.
S. Holm (1979). A simple sequentially rejective multiple test procedure. Scand. J. Statist.. Vol. 6: 6570.
mt.maxT
, mt.minP
,
mt.plot
, mt.reject
, 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 pvalues and adjusted pvalues for maxT procedure res1<mt.maxT(smallgd,classlabel) rawp<res1$rawp[order(res1$index)] # Permutation adjusted pvalues for simple multiple testing procedures procs<c("Bonferroni","Holm","Hochberg","SidakSS","SidakSD","BH","BY") res2<mt.rawp2adjp(rawp,procs)