rags2ridges (version 2.2.5)

covML: 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

Value

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

See Also

ridgeP

Examples

# NOT RUN {
## 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)

# }