comp.unadjp {DEDS}  R Documentation 
This function computes permuation based unadjusted p values for a selected test statistic, e.g., one or twosample tstatitics, Fstatistics, SAM, Fold change, for each row of a matrix.
comp.unadjp(X, L, B = 1000, test = c("t", "fc", "sam", "f"), 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 read.table .

L 
A vector of integers corresponding to observation (column) class labels. For k classes, the labels must be integers between 0 and k1. 
B 
The number of permutations. For a complete enumeration,
B should be 0 (zero) or any number not less than the total
number of permutations.

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. If test="t" , for oneclass, the tests are based on onesample
tstatistics; for twoclass, the tests are based on twosample tstatistics
(unequal variances). If test="f" , the tests are based on Fstatistics.If test="fc" , the tests are based on fold changes among classes.If test="sam" , the tests are based on SAMstatistics.

tail 
A character string specifying the type of rejection region. If side="abs" , twotailed tests, the null hypothesis is rejected for large absolute values of the test statistic.If side="higher" , onetailed tests, the null hypothesis is rejected for large values of the test statistic.If side="lower" , onetailed tests, the null hypothesis is rejected for small values of the test statistic.

extra 
Extra parameter need for the test specified; see
deds.genExtra . 
The function comp.unadjp
computes unadjusted p values using
a permutation scheme.
A vector of unadjusted p values for each row of the matrix.
Yuanyuan Xiao, yxiao@itsa.ucsf.edu,
Jean Yee Hwa Yang, jean@biostat.ucsf.edu.
X < matrix(rnorm(1000,0,0.5), nc=10) L < rep(0:1,c(5,5)) # genes 110 are differentially expressed X[1:10,6:10]<X[1:10,6:10]+1 # t statistics unadjp.t < comp.unadjp(X, L, test="t")