# tindep: Standardized scores for two independent samples In PCS: Calculate the probability of correct selection (PCS)

## Description

Calculate the independent two-sample Welch t-statistics for k populations simultaneously. This function is used in PCS.bootstrap.np. It may also be used to summarize data from two sample experiments for use in PCS.exact and PCS.bootstrap.par.

## Usage

 `1` ```tindep(X, Y, flag = 0) ```

## Arguments

 `X` k by n1 matrix of data. k is the number of populations and n1 the sample size of the first treatment. `Y` k by n2 matrix of data. k is the number of populations and n2 the sample size of the second treatment. `flag` Determines whether the output is t-statistics (flag=0) or t-statistics with p-values (flag!=0)

## Details

X & Y are the data matrices for input. When called inside of the function PCS.bootstrap.np, or when used to obtain t-statistics for use with PCS.exact and PCS.bootstrap.par, setting 'flag'=0 will return the needed vector of t-statistics. If flag!=0, then the function 'mt.rawp2adjp' from the multtest library is called, producing multiple comparison adjustd pvalues ("Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BH", "BY"). The t-statistics and p-values are stored in a matrix.

## Value

If flag = 0, the result is a k by 1 vector of t-statistics. If flag != 0, the result is a k by 7 vector of t-statistics and pvalues (see details).

## Author(s)

Jason Wilson <[email protected]>

`PCS.boot.np`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```### Small example k=20 #number of populations n=10 #sample size SD=.1 #standard deviation theta = seq(0,6,length.out=k) X1 = rnorm(k*n,0,SD) #Sample 1 X1 = matrix(X1,nrow=k,ncol=n,byrow=FALSE) X2 = rnorm(k*n,theta,SD) #Sample 2, shifted X2 = matrix(X2,nrow=k,ncol=n,byrow=FALSE) tindep(X1,X2, flag=1) ### Microarray example #require(multtest) data(golub) sub = 500 #Subset index for speed golub.Tp = tindep(golub[1:sub,1:27], golub[1:sub,28:38], flag=0) #Obtain t-statistics + p-values ans = tindep(golub[1:sub,1:27], golub[1:sub,28:38], flag=1) #Obtain t-statistics golub.T = sort(abs(ans[,1])) ```