estimMMM: This function performs the model selection and the parameter...

In MGMM: Mutliple Clustering with Mixture Model per Blocs

Description

This function performs the model selection and the parameter inference.

Usage

estimMMM(x, gmax = NULL, nbBlocs = NULL, modellist = NULL, nbinit = 20,
  nbCPU = 1, criterion = "PenLike", pen = 0.5 * log(nrow(x)))

Arguments

x	data.frame. Observations to cluster (continuous variables must be "numeric" and categorical variables must be "factor").
gmax	numeric. Maximum number of component (not used if modellist is specified).
nbBlocs	numeric. Number of blocs of variables (not used if modellist is specified).
modellist	matrix. Models to consider. The number of columns specifies the number of blocs. Each row corresponds to a model andx specifies the number of components per blocs.
nbinit	numeric. Number of random initializations.
nbCPU	numeric. Number of cores (not for windows).
criterion	character. Criterion used for model selection (must be "PenLike" or "MICL")
pen	numeric. Value of the penalty (only used if criterion="PenLike"). BIC corresponds to pen=0.5 * log(nrow(x)), AIC correponds to pen=1.

Value

List

Examples

# Sample data from 2 blocs of bivariate Gaussian mixture with two components
# slot x: observation, slot z: partitions
obs <- rMGMM()
plot(obs$x, col=obs$z[,1])
plot(obs$x, col=obs$z[,2])
# Model selection with three components at the maximum, two blocks and BIC.
res <- estimMMM(obs$x, 2, 2)

MGMM documentation built on May 2, 2019, 5:19 p.m.