Description Usage Arguments Details Value Author(s) See Also Examples
This function computes permutation based unadjusted p values for a selected test statistic, e.g., one or twosample tstatistics, Fstatistics, SAM, Fold change, for each row of a matrix.
1  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 
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,

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

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, [email protected],
Jean Yee Hwa Yang, [email protected].
1 2 3 4 5 6 7 8  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")

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