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mt.rawp2adjp.LPE(rawp, proc=c("Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BH", "BY"))
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"
.
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),]
.
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 (In preparation). Fast algorithm for resampling-based $p$-value adjustment in multiple testing.
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.
lpe
# Loading the library and the data
library(LPE)
data(Ley)
dim(Ley)
# Gives 12488*7
# First column is ID.
# Subsetting the data
subset.Ley <- Ley[1:1000,]
subset.Ley[,2:7] <- preprocess(subset.Ley[,2:7],data.type="MAS5")
# Finding the baseline distribution of condition 1 and 2.
var.1 <- baseOlig.error(subset.Ley[,2:4], q=0.01)
var.2 <- baseOlig.error(subset.Ley[,5:7], q=0.01)
# Applying LPE
lpe.result <- lpe(subset.Ley[,2:4],subset.Ley[,5:7], var.1, var.2,
probe.set.name=subset.Ley[,1])
fdr.BH <- fdr.adjust(lpe.result, adjp="BH")
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