HDTD (version 1.6.0)

meanmat.ts: Nonparametric Tests for the Mean Matrix

Description

This function performs hypothesis testing for the mean matrix.

Usage

meanmat.ts(datamat, N, group.sizes, voi = "columns")

Arguments

datamat

numeric matrix containing the transposable data.

N

positive integer number indicating the sample size, i.e., the number of subjects.

group.sizes

numeric vector indicating the group sizes under the null hypothesis.

voi

character indicating if the test will be applied to the row or column variables. Options include "rows" or "columns".

Value

Returns a list with components:

statistic

the value of the test statistic.

p.value

the corresponding p-value.

voi

the set of variables that the test was applied to.

n.groups

the number of groups under the null hypothesis.

group.sizes

the size of each group under the null hypothesis.

N

the sample size.

n.rows

the number of row variables.

n.cols

the number of column variables.

Details

It is assumed that there are nrow(datamat) row variables and ncol(datamat)/N column variables in datamat. Further, datamat should be written in such a way that every ncol(datamat)/N consecutive columns belong to the same subject and the order of the column variables in each block is preserved across subjects.

References

Touloumis, A., Tavare, S. and Marioni, J.C. (2015). Testing the Mean Matrix in High-Dimensional Transposable Data. Biometrics, 71, 157-166.

Examples

data(VEGFmouse)
## Testing if there is no tissue effect on the mean expression level for each gene.
tistest <- meanmat.ts(VEGFmouse,40,group.sizes=9,voi="columns")
tistest
## Testing if the adrenal and the cerebrum tissues have the same mean vector.
tistest2 <- meanmat.ts(VEGFmouse,40,group.sizes=c(2,rep(1,7)),voi="columns")
tistest2