comp.adjp {DEDS} | R Documentation |
This function computes permutation based step-down maxT adjusted p values for a
selected test statistic, e.g., one- or two-sample
t-statistics, F-statistics, SAM, Fold change, moderated t-statistics
and moderated F-statistics, for each row of a matrix. The procedure is
based on codes from mt.maxT
and described in
Westfall & Young (1993).
comp.adjp(X, L, B = 1000, test = c("t", "fc", "sam", "f", "modt", "modf"), tail = c("abs", "lower", "higher"), extra = NULL)
X |
A matrix, with m rows corresponding to variables
(hypotheses) andn columns corresponding to observations.
In the case of gene expression data, rows correspond to genes and
columns to mRNA samples. The data can be read using |
L |
A vector of integers corresponding to observation (column) class labels. For k classes, the labels must be integers between 0 and k-1. |
B |
The number of permutations. For a complete enumeration,
|
test |
A character string specifying the statistic to be
used to test the null hypothesis of no association between the
variables and the class labels. |
tail |
A character string specifying the type of rejection region. |
extra |
Extra parameter need for the test specified; see
|
see mt.maxT
.
A matrix of the following columns:
order |
order of rows (genes) based on statistics. |
stat |
a vector of statistics. |
unadj.p |
a vector of unadjusted p values. |
adj.p |
a vector of adjusted p values. |
Yuanyuan Xiao, yxiao@itsa.ucsf.edu,
Jean Yee Hwa Yang, jean@biostat.ucsf.edu.
comp.unadjp
, comp.fdr
, comp.stat
X <- matrix(rnorm(1000,0,0.5), nc=10) L <- rep(0:1,c(5,5)) # genes 1-10 are differentially expressed X[1:10,6:10]<-X[1:10,6:10]+1 # t statistics unadjp.t <- comp.adjp(X, L, test="t")