| mt.rawp2adjp {multtest} | R Documentation |
This function computes adjusted p-values for simple multiple testing procedures from a vector of raw (unadjusted) p-values. The procedures include the Bonferroni, Holm (1979), Hochberg (1988), and Sidak procedures for strong control of the family-wise Type I error rate (FWER), and the Benjamini & Hochberg (1995) and Benjamini & Yekutieli (2001) procedures for (strong) control of the false discovery rate (FDR). The less conservative adaptive Benjamini & Hochberg (2000) and two-stage Benjamini & Hochberg (2006) FDR-controlling procedures are also included.
mt.rawp2adjp(rawp, proc=c("Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD",
"BH", "BY","ABH","TSBH"), alpha = 0.05, na.rm = FALSE)
rawp |
A vector of raw (unadjusted) p-values for each
hypothesis under consideration. These could be nominal
p-values, for example, from t-tables, or permutation
p-values as given in |
proc |
A vector of character strings containing the names of the
multiple testing procedures for which adjusted p-values are to
be computed. This vector should include any of the following:
Adjusted p-values are computed for simple FWER- and FDR- controlling procedures based on a vector of raw (unadjusted) p-values by one or more of the following methods:
|
alpha |
A nominal type I error rate, or a vector of error
rates, used for estimating the number of true null hypotheses in the
two-stage Benjamini & Hochberg procedure ( |
na.rm |
An option for handling |
A list with components:
adjp |
A matrix of adjusted p-values, with rows corresponding to hypotheses and columns to multiple testing procedures. Hypotheses are sorted in increasing order of their raw (unadjusted) p-values. |
index |
A vector of row indices, between 1 and
|
h0.ABH |
The estimate of the number of true null hypotheses as proposed
by Benjamini & Hochberg (2000) used when computing adjusted p-values
for the |
h0.TSBH |
The estimate (or vector of estimates) of the number of true
null hypotheses as proposed by Benjamini et al. (2006) when computing adjusted
p-values for the |
Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine,
Yongchao Ge, yongchao.ge@mssm.edu,
Houston Gilbert, http://www.stat.berkeley.edu/~houston.
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: 289-300.
Y. Benjamini and Y. Hochberg (2000). On the adaptive control of the false discovery rate in multiple testing with independent statistics. J. Behav. Educ. Statist. Vol 25: 60-83.
Y. Benjamini and D. Yekutieli (2001). The control of the false discovery rate in multiple hypothesis testing under dependency. Annals of Statistics. Vol. 29: 1165-88.
Y. Benjamini, A. M. Krieger and D. Yekutieli (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika. Vol. 93: 491-507.
S. Dudoit, J. P. Shaffer, and J. C. Boldrick (2003). Multiple
hypothesis testing in microarray experiments. Statistical Science. Vol. 18: 71-103.
S. Dudoit, H. N. Gilbert, and M. J. van der Laan (2008).
Resampling-based empirical Bayes multiple testing procedures for controlling generalized tail probability and expected value error rates: Focus on the false discovery rate and simulation study. Biometrical Journal, 50(5):716-44. http://www.stat.berkeley.edu/~houston/BJMCPSupp/BJMCPSupp.html.
Y. Ge, S. Dudoit, and T. P. Speed (2003). Resampling-based multiple testing for microarray data analysis. TEST. Vol. 12: 1-44 (plus discussion p. 44-77).
Y. Hochberg (1988). A sharper Bonferroni procedure for multiple tests of significance, Biometrika. Vol. 75: 800-802.
S. Holm (1979). A simple sequentially rejective multiple test procedure. Scand. J. Statist.. Vol. 6: 65-70.
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 p-values and adjusted p-values for maxT procedure
res1<-mt.maxT(smallgd,classlabel)
rawp<-res1$rawp[order(res1$index)]
# Permutation adjusted p-values for simple multiple testing procedures
procs<-c("Bonferroni","Holm","Hochberg","SidakSS","SidakSD","BH","BY","ABH","TSBH")
res2<-mt.rawp2adjp(rawp,procs)