mt.rawp2adjp(rawp, proc=c("Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD",
"BH", "BY","ABH","TSBH"), alpha = 0.05, na.rm = FALSE)
mt.maxT
and mt.minP
. If the
mt.maxT
or mt.minP
functions are used, raw
$p$-values should be given in the original data order,
rawp[order(index)]
."Bonferroni"
, "Holm"
, "Hochberg"
,
"SidakSS"
, "SidakSD"
, "BH"
, "BY"
,
"ABH"
, "TSBH"
.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:
"BH"
. The adjusted $p$-values are $a$-dependent, therefore alpha
must be set in the function arguments when using this procedure."TSBH"
). Default is 0.05.NA
values in a list of raw $p$-values. If
FALSE
, the number of hypotheses considered is the length of the vector
of raw $p$-values. Otherwise, if TRUE
, the number of hypotheses is
the number of raw $p$-values which were not NA
s.length(rawp)
, where rows are sorted according to
their raw (unadjusted) $p$-values. To obtain the adjusted
$p$-values in the original data order, use
adjp[order(index),]
."ABH"
procedure (see Dudoit et al., 2007)."TSBH"
procedure. (see Dudoit et al., 2007).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)
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