mclust (version 5.4.6)

covw: Weighted means, covariance and scattering matrices conditioning on a weighted matrix

Description

Compute efficiently (via Fortran code) the means, covariance and scattering matrices conditioning on a weighted or indicator matrix

Usage

covw(X, Z, normalize = TRUE)

Arguments

X

A \((n x p)\) data matrix, with \(n\) observations on \(p\) variables.

Z

A \((n x G)\) matrix of weights, with \(G\) number of groups.

normalize

A logical indicating if rows of Z should be normalized to sum to one.

Value

A list with the following components:

mean

A \((p x G)\) matrix of weighted means.

S

A \((p x p x G)\) array of weighted covariance matrices.

W

A \((p x p x G)\) array of weighted scattering matrices.

Examples

# NOT RUN {
# Z as an indicator matrix
X <- iris[,1:4]
Z <- unmap(iris$Species)
str(covw(X, Z))
# Z as a matrix of weights
mod <- Mclust(X, G = 3, modelNames = "VVV")
str(covw(X, mod$z))
# }