ridgeS: Ridge estimation for high-dimensional precision matrices

In CFWP/rags2ridges: Ridge Estimation of Precision Matrices from High-Dimensional Data

Ridge estimation for high-dimensional precision matrices

Description

This function is now deprecated. Please use ridgeP instead.

Usage

ridgeS(S, lambda, type = "Alt", target = default.target(S))

Arguments

S	Sample covariance matrix.
lambda	A numeric representing the value of the penalty parameter.
type	A character indicating the type of ridge estimator to be used. Must be one of: "Alt", "ArchI", "ArchII".
target	A target matrix (in precision terms) for Type I ridge estimators.

Details

See ridgeP.

Value

Function returns a regularized precision matrix.

Author(s)

Carel F.W. Peeters <[e-mail]>, Wessel N. van Wieringen

See Also

ridgeP

Examples

## Obtain some (high-dimensional) data
p = 25
n = 10
set.seed(333)
X = matrix(rnorm(n*p), nrow = n, ncol = p)
colnames(X)[1:25] = letters[1:25]
Cx <- covML(X)

## Obtain regularized precision matrix
ridgeS(Cx, lambda = 10, type = "Alt")

CFWP/rags2ridges documentation built on Oct. 21, 2023, 10:19 a.m.