MatTrans.init {MatTransMix} | R Documentation |
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)