covmat.hat: Estimation of the Row and of the Column Covariance Matrices.

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

Description

This function provides the row and/or column covariance matrix estimators.

Usage

covmat.hat(datamat, N, shrink = "both", centered = FALSE, voi = "both")

Arguments

datamat	numeric matrix containing the transposable data.
N	positive integer number indicating the sample size, i.e., the number of subjects.
shrink	character indicating if shrinkage estimation should be performed. Options include 'rows', 'columns', 'both' and 'none'.
centered	logical indicating if the transposable data are centered. Options include TRUE or FALSE.
voi	character indicating if the row, column or both covariance matrices should be printed. Options include 'rows', 'columns' and 'both'.

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.

For identifiability reasons, the trace of the row covariance matrix is set equal to its dimension. If you want to place the equivalent restriction on the column covariance matrix, interchange the role of row and column variables by utilizing the function transposedata.

Value

Returns a list with components:

rows.covmat	the estimated row covariance matrix.
rows.intensity	the estimated row intensity.
cols.covmat	the estimated column covariance matrix.
cols.intensity	the estimated column intensity.
N	the sample size.
n.rows	the number of row variables.
n.cols	the number of column variables.
shrink	character indicating if shrinkage estimation was performed.
centered	logical indicating if the transposable data were centered.

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)
# Estimating the gene and tissue covariance matrices.
est_cov_mat <- covmat.hat(datamat = VEGFmouse, N = 40)
est_cov_mat

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