MatTransMix (version 0.1.18)

MatTrans.init: 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)

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

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.

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)