MatTrans.init: Initialization for the EM algorithm for matrix clustering

In MatTransMix: Clustering with Matrix Gaussian and Matrix Transformation Mixture Models

Initialization for the EM algorithm for matrix clustering

Description

Runs the initialization for the EM algorithm for matrix clustering

Usage

MatTrans.init(Y, K, n.start = 10, scale = 1)

Arguments

Y	dataset of random matrices (p x T x n), n random matrices of dimensionality (p x T)
K	number of clusters
n.start	initial random starts
scale	scaling parameter

Details

Random starts are used to obtain different starting values. The number of clusters, the skewness parameters, and number of random starts need to be specified. In the case when transformation parameters are not provided, the function runs the EM algorithm without any transformations, i.e., it is equivalent to the EM algorithm for a matrix Gaussian mixture. Notation: n - sample size, p x T - dimensionality of the random matrices, K - number of mixture components.

Value

scale	scale parameter set by the user
result	parsimonious models
model	model types
loglik	log likelihood values
bic	bic values
best.result	best parsimonious model
best.model	best model type
best.loglik	best logliklihood
best.bic	best bic
trans	transformation type

Examples

set.seed(123)
data(crime)
Y <- crime$Y[c(2,7),,] / 1000
p <- dim(Y)[1]
T <- dim(Y)[2]
n <- dim(Y)[3]
K <- 2
init <- MatTrans.init(Y, K = K, n.start = 2)

MatTransMix documentation built on April 12, 2025, 2:24 a.m.