matrixT {oompaBase}

Multiple T Tests by Matrix Multiplication

Description

Utility functions for computing vectors of row-by-row means, variances, and t-statistics.

Usage

matrixMean(x,na.rm=FALSE)
matrixVar(x, xmean, na.rm=FALSE)
matrixT(m, v, na.rm=FALSE)
matrixUnequalT(m, v)
matrixPairedT(m, v, pf)

Arguments

x

a matrix

xmean

a numeric vector or single-column matrix

m

a matrix

na.rm

a logical value indicating whether means, variances, and t-statistics should be computed after omitting NA values from individual rows of the data matrix.

v

a logical vector of length equal to the number of columns of m

pf

a numerical vector of length equal to the number of columns of m, indicating which samples should be paired

Value

matrixMean returns a single-column matrix containing the row-by-row means of x.

matrixVar returns a single-column matrix containing the row-by-row means of x, assuming that xmean contains the corresponding mean values.

matrixT returns a single-column matrix of t-statistics from a two-sample t-test comparing the columns for which v is true to those for which v is false.

matrixPairedT returns a single-column matrix of t-statistics from a paired t-test.

matrixUnequalT returns a list with two components: tt is a single-column matrix of t-statistics from a two-sample unequal variance t-test comparing the columns for which v is true to those for which v is false, and df is a single-column matrix of the degrees of freedom associated with each row..

Author(s)

Kevin R. Coombes <[e-mail]>

Examples

ng <- 1000
ns <- 50
dat <- matrix(rnorm(ng*ns), ncol=ns)
clas <- factor(rep(c('Good', 'Bad'), each=25))
myMean <- matrixMean(dat)
myVar  <- matrixVar(dat, myMean)
plot(myMean, myVar)


myT <- matrixT(dat, clas)
hist(myT)

rm(ng, ns, dat, myMean, myVar, myT)