flexmix (version 2.3-19)

FLXMCfactanal: Driver for Mixtures of Factor Analyzers

Description

This driver for flexmix implements estimation of mixtures of factor analyzers using ML estimation of factor analysis implemented in factanal in each M-step.

Usage

FLXMCfactanal(formula = . ~ ., factors = 1, ...)

Value

FLXMCfactanal returns an object of class FLXM.

Arguments

formula

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

factors

Integer specifying the number of factors in each component.

...

Passed to factanal

Author

Bettina Gruen

Warning

This does not implement the AECM framework presented in McLachlan and Peel (2000, p.245), but uses the available functionality in R for ML estimation of factor analyzers. The implementation therefore is only experimental and has not been well tested.

Please note that in general a good initialization is crucial for the EM algorithm to converge to a suitable solution for this model class.

References

G. McLachlan and D. Peel. Finite Mixture Models, 2000. John Wiley and Sons Inc.

See Also

flexmix

Examples

## Reproduce (partly) Table 8.1. p.255 (McLachlan and Peel, 2000)
if (require("gclus")) {
  data("wine", package = "gclus")
  wine_data <- as.matrix(wine[,-1])
  set.seed(123)
  wine_fl_diag <- initFlexmix(wine_data ~ 1, k = 3, nrep = 10,
                              model = FLXMCmvnorm(diagonal = TRUE))
  wine_fl_fact <- lapply(1:4, function(q) flexmix(wine_data ~ 1, model =
                          FLXMCfactanal(factors = q, nstart = 3),
                          cluster = posterior(wine_fl_diag)))
  sapply(wine_fl_fact, logLik)
  ## FULL
  set.seed(123)
  wine_full <- initFlexmix(wine_data ~ 1, k = 3, nrep = 10,
                           model = FLXMCmvnorm(diagonal = FALSE))
  logLik(wine_full)
  ## TRUE
  wine_true <- flexmix(wine_data ~ 1, cluster = wine$Class,
                       model = FLXMCmvnorm(diagonal = FALSE))
  logLik(wine_true)
}