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

Description Usage Arguments Details Value Author(s) References Examples

View source: R/covmat.hat.R

Description

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

Usage

1
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

1
2
3
4
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.