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)
read.table
.
B
should be 0 (zero) or any number not less than the total
number of permutations.
test="t"
, for one-class, the tests are based on one-sample
t-statistics; for two-class, the tests are based on two-sample t-statistics
(unequal variances).
If test="f"
, the tests are based on F-statistics.
If test="fc"
, the tests are based on fold changes among classes.
If test="sam"
, the tests are based on SAM-statistics.
If test="modt"
, the tests are based on moderated
t-statistics.
If test="modf"
, the tests are based on moderated F-statistics.
side="abs"
, two-tailed tests, the null hypothesis is rejected for large absolute values of the test statistic.
If side="higher"
, one-tailed tests, the null hypothesis is rejected for large values of the test statistic.
If side="lower"
, one-tailed tests, the null hypothesis is rejected for small values of the test statistic.
deds.genExtra
.mt.maxT
.
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")
Run the code above in your browser using DataLab