covw: Weighted means, covariance and scattering matrices...

In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

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.

Author(s)

M. Fop and L. Scrucca

Examples

# 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))

mclust documentation built on May 29, 2024, 8:06 a.m.