targetselection: Target Matrix Selection

In AnestisTouloumis/ShrinkCovMat: Shrinkage Covariance Matrix Estimators

Target Matrix Selection

Description

Implements the rule of thumb proposed by Touloumis (2015) for target matrix selection. If the estimated optimal shrinkage intensities of the three target matrices are of similar magnitude, then the average and the range of the sample variances should be inspected in order to adopt the most plausible target matrix.

Usage

targetselection(data, centered = FALSE)

Arguments

data	a numeric matrix containing the data.
centered	a logical indicating if the mean vector is the zero vector.

Details

The rows of the data matrix data correspond to variables and the columns to subjects.

Value

Prints the estimated optimal shrinkage intensities, the range and average of the sample variances and returns an object of the class 'targetsel' that has components:

lambda_hat_spherical	The estimated optimal shrinkage intensity for the spherical target matrix.
lambda_hat_identity	The estimated optimal shrinkage intensity for the identity target matrix.
lambda_hat_diagonal	The estimated optimal intensity for the diagonal target matrix.
range	The range of the sample variances.
average	The average of the sample variances.

Author(s)

Anestis Touloumis

References

Touloumis, A. (2015) Nonparametric Stein-type Shrinkage Covariance Matrix Estimators in High-Dimensional Settings. Computational Statistics & Data Analysis 83, 251–261.

Examples

data(colon)
normal_group <- colon[, 1:40]
targetselection(normal_group)
## Similar intensities, the range of the sample variances is small and the
## average is not close to one. The spherical matrix seems to be the
## most suitable target matrix for the normal group.

tumor_group <- colon[, 41:62]
targetselection(tumor_group)
## Similar intensities, the range of the sample variances is small and the
## average is not close to one. The spherical matrix seems to be the
## most suitable target matrix for the colon group.

AnestisTouloumis/ShrinkCovMat documentation built on July 30, 2023, 7:38 a.m.