trainControl {HDclust}

Parameters for HMM-VB training.

Description

This function creates a list with parameters for estimating an HMM-VB, which is used as an argument for hmmvbTrain.

Usage

trainControl(ninit0 = 1, ninit1 = 0, ninit2 = 0, epsilon = 1e-04,
  diagCov = FALSE)

Arguments

ninit0

The number of initializations for default scheme 0, under which the k-means clustering for entire dataset is used to initialize the model.

ninit1

The number of initializations for default scheme 1, under which the k-means clustering for a subset of data is used to initialize the model.

ninit2

The number of initializations for default scheme 2, under which a random subset of data is used as cluster centroids to initialize the model.

epsilon

Stopping criteria for Baum-Welch algorithm. Should be a small number in range (0,1).

diagCov

A logical value indicating whether or not variable block covariance matrices will be diagonal.

Value

The named list with parameters.

See Also

hmmvbTrain

Examples

# setting up multiple initialization schemes
Vb <- vb(1, dim=4, numst=2)
set.seed(12345)
hmmvb <- hmmvbTrain(iris[,1:4], VbStructure=Vb, 
          trControl=trainControl(ninit0=2, ninit1=2, ninit2=2))
show(hmmvb)

# forcing diagonal covariance matrices
Vb <- vb(1, dim=4, numst=2)
set.seed(12345)
hmmvb <- hmmvbTrain(iris[,1:4], VbStructure=Vb, 
          trControl=trainControl(diagCov=TRUE))
show(hmmvb)