rags2ridges (version 2.2.6)

evaluateS: Evaluate numerical properties square matrix

Description

Function that evaluates various numerical properties of a square input matrix. The intended use is to evaluate the various numerical properties of what is assumed to be a covariance matrix. Another use is to evaluate the various numerical properties of a (regularized) precision matrix.

Usage

evaluateS(S, verbose = TRUE)

Value

symm

A logical indicating if the matrix is symmetric.

realEigen

A logical indicating if the eigenvalues are real.

posEigen

A logical indicating if the eigenvalues are strictly positive.

dd

A logical indicating if the matrix is diagonally dominant.

trace

A numerical giving the value of the trace.

det

A numerical giving the value of the determinant.

condNumber

A numerical giving the value of the spectral condition number.

Arguments

S

Covariance or (regularized) precision matrix.

verbose

A logical indicating if output should be printed on screen.

Author

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

Details

The function evaluates various numerical properties of a covariance or precision input matrix. The function assesses if the input matrix is symmetric, if all its eigenvalues are real, if all its eigenvalues are strictly positive, and if it is a diagonally dominant matrix. In addition, the function calculates the trace, the determinant, and the spectral condition number of the input matrix. See, e.g., Harville (1997) for more details on the mentioned (numerical) matrix properties.

References

Harville, D.A.(1997). Matrix algebra from a statistician's perspective. New York: Springer-Verlag.

See Also

covML, 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)

## Evaluate numerical properties covariance matrix
## Obtain, e.g., value trace
Seval <- evaluateS(Cx); Seval
Seval$trace

## Evaluate numerical properties precision matrix after regularization
P <- ridgeP(Cx, lambda = 10, type = 'Alt')
Peval <- evaluateS(P); Peval