flexmix (version 2.2-11)

stepFlexmix: Run FlexMix Repeatedly

Description

Runs flexmix repeatedly for different numbers of components and returns the maximum likelihood solution for each.

Usage

stepFlexmix(..., k=NULL, nrep=3, verbose=TRUE, drop=TRUE,
            unique=FALSE)
## S3 method for class 'stepFlexmix,missing':
plot(x, y, what=c("AIC", "BIC", "ICL"),
  xlab=NULL, ylab=NULL, legend="topright", ...)
## S3 method for class 'stepFlexmix':
getModel(object, which="BIC")
## S3 method for class 'stepFlexmix':
unique(x, incomparables = FALSE, ...)

Arguments

...

Passed to flexmix (or matplot in the plot method).

k

A vector of integers passed in turn to the k argument of flexmix.

nrep

For each value of k run flexmix nrep times and keep only the solution with maximum likelihood.

verbose

If TRUE, show progress information during computations.

drop

If TRUE and k is of length 1, then a single flexmix object is returned instead of a "stepFlexmix" object.

unique

If TRUE, then unique() is called on the result, see below.

x, object

An object of class "stepFlexmix".

y

Not used.

what

Character vector naming information criteria to plot. Functions of the same name must exist, which take a stepFlexmix object as input and return a numeric vector like AIC,stepFlexmix-method (see examples below).

xlab,ylab

Graphical parameters.

legend

If not FALSE and what contains more than 1 element, a legend is placed at the specified location, see legend for details.

which

Number of model to get. If character, interpreted as number of components or name of an information criterion.

incomparables

A vector of values that cannot be compared. Currently, 'FALSE' is the only possible value, meaning that all values can be compared.

Value

• An object of class "stepFlexmix" containing the best models with respect to the log likelihood for the different number of components in a slot if length(k)>1, else directly an object of class "flexmix".

  If unique=FALSE, then the resulting object contains one model per element of k (which is the number of clusters the EM algorithm started with). If unique=TRUE, then the result is resorted according to the number of clusters contained in the fitted models (which may be less than the number with which the EM algorithm started), and only the maximum likelihood solution for each number of fitted clusters is kept. This operation can also be done manually by calling unique() on objects of class "stepFlexmix".

References

Friedrich Leisch. FlexMix: A general framework for finite mixture models and latent class regression kin R. Journal of Statistical Software, 11(8), 2004. http://www.jstatsoft.org/v11/i08/

Examples

data("Nclus")
set.seed(511)

## try 5 times for k=4
ex1 <- stepFlexmix(Nclus~1, k=4, model=FLXMCmvnorm(diagonal=FALSE), nrep=5)
ex1

## now 3 times each for k=2:6, specify control parameter
ex2 <- stepFlexmix(Nclus~1, k=2:6, model=FLXMCmvnorm(diagonal=FALSE),
                   control=list(minprior=0), nrep=3)
ex2
plot(ex2)

## get BIC values
BIC(ex2)

## get smallest model
getModel(ex2, which=1)

## get model with 3 components
getModel(ex2, which="3")

## get model with smallest ICL (here same as for AIC and BIC: true k=4)
getModel(ex2, which="ICL")

## now 1 time each for k=2:6, with larger minimum prior
ex3 <- stepFlexmix(Nclus~1, k=2:6, model=FLXMCmvnorm(diagonal=FALSE),
                   control=list(minprior=0.1), nrep=1)
ex3

## keep only maximum likelihood solution for each unique number of
## fitted clusters:
unique(ex3)