FLXMCregnorm {flexord}R Documentation

FlexMix Driver for Regularized Multivariate Normal Mixtures

Description

This model driver implements the regularization method as introduced by Fraley and Raftery (2007) for univariate normal mixtures. Default parameters for the regularization according to that paper may be obtained using FLXMCregnorm_defaults(). We extend this to the multivariate case assuming independence between variables within components, i.e., we only implement the special case where the covariance matrix is diagonal. For more general applications of normal mixtures see package mclust.

Usage

FLXMCregnorm(formula = . ~ ., params)

Arguments

formula

A formula which is interpreted relative to the formula specified in the call to flexmix::flexmix() using stats::update.formula(). Only the left-hand side (response) of the formula is used. Default is to use the original model formula specified in flexmix::flexmix().

params

Prior parameters for normal mixtures. You may obtain default values according to Fraley and Raftery (2007) using FLXMCregnorm_defaults(). As the prior depends on the number of components it is probably not advisable to run stepFlexmix with more than one value of k at a time.

Details

For the regularization the conjugate prior distributions for the normal distribution are used, which are:

Value

An object of class "FLXC".

References

See Also

FLXMCregnorm_defaults

Examples

library("flexmix")
library("flexord")
library("flexclust")

# example data
data("iris", package = "datasets")
my_iris <- subset(iris, select=setdiff(colnames(iris), "Species")) |>
    as.matrix()

# cluster one model with a scale parameter similar to the default for 3 components
params <- FLXMCregnorm_defaults(my_iris, zeta_p = c(0.23, 0.06, 1.04, 0.19))
m1 <- stepFlexmix(my_iris ~ 1, k = 3, 
    model=FLXMCregnorm(params = params))
summary(m1)

# rand index of clusters vs species
randIndex(clusters(m1), iris$Species)

# cluster one model with default scale parameter
params <- FLXMCregnorm_defaults(my_iris, k = 3)
m2 <- stepFlexmix(my_iris ~ 1, k = 3,
    model = FLXMCregnorm(params = params))
summary(m2)

# rand index of clusters vs species
randIndex(clusters(m2), iris$Species)

# rand index between both models (should be >= 0.8)
randIndex(clusters(m1), clusters(m2))

[Package flexord version 1.0.0 Index]