msir.regularizedSigma: Regularized estimate of predictors covariance matrix.

In luca-scr/msir: Model-Based Sliced Inverse Regression

Regularized estimate of predictors covariance matrix.

Description

This function computes a regularized version of the covariance matrix of the predictors. Among the possible models the one which maximizes BIC is returned.

Usage

msir.regularizedSigma(x, inv = FALSE, model = c("XII", "XXI", "XXX"))

Arguments

x	Ahe predictors data matrix.
inv	A logical specifying what must be returned. If TRUE the inverse of the estimated covariance matrix is returned, otherwise the estimated covariance matrix (default).
model	A character string specifying the available models: XII: diagonal equal variances XXI: diagonal unequal variances XXX: full covariance matrix

Value

A (p \times p) covariance matrix estimate.

Author(s)

Luca Scrucca [e-mail]

See Also

msir

luca-scr/msir documentation built on March 2, 2024, 10:05 p.m.