covML: Maximum likelihood estimation of the covariance matrix

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

Maximum likelihood estimation of the covariance matrix

Description

Function that gives the maximum likelihood estimate of the covariance matrix.

Usage

covML(Y, cor = FALSE)

Arguments

Y	Data matrix. Variables assumed to be represented by columns.
cor	A logical indicating if the correlation matrix should be returned

Details

The function gives the maximum likelihood (ML) estimate of the covariance matrix. The input matrix Y assumes that the variables are represented by the columns. Note that when the input data is standardized, the ML covariance matrix of the scaled data is computed. If a correlation matrix is desired, use cor = TRUE.

Value

Function returns the maximum likelihood estimate of the covariance matrix. In case cor = TRUE, the correlation matrix is returned.

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]

## Obtain ML estimate covariance matrix
Cx <- covML(X)

## Obtain correlation matrix
Cx <- covML(X, cor = TRUE)

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