meanmat.hat: Estimation the Mean Matrix

In AnestisTouloumis/HDTD: Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD)

Description

This function estimates the mean matrix.

Usage

meanmat.hat(datamat, N, group.sizes = NULL, group.vars = NULL)

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 size of the row or column groups that share the same mean vector. It should be used only when group.vars='rows' or 'columns'.
group.vars	character indicating that the mean matrix can be simplified over the row or column variables. Options include 'rows' or 'columns'.

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.

Value

Returns a list with components:

estmeanmat	the estimated mean matrix.
N	the sample size.
n.rows	the number of row variables.
n.cols	the number of column variables.

Author(s)

Anestis Touloumis

References

Touloumis, A., Marioni, J. C. and Tavare, S. (2016) HDTD: Analyzing multi-tissue gene expression data. Bioinformatics 32, 2193–2195.

Examples

data(VEGFmouse)
## The sample mean matrix of the VEGF mouse data.
sample_mean <- meanmat.hat(datamat = VEGFmouse, N = 40)
sample_mean
sample_mean$estmeanmat

AnestisTouloumis/HDTD documentation built on Aug. 2, 2021, 2:15 p.m.