flexmix (version 0.9-0)

flexmix: Flexible Mixture Modeling

Description

FlexMix implements a general framework for finite mixtures of regression models. Parameter estimation is performed using the EM algorithm: the E-step is implemented by flexmix, while the user can specify the M-step.

Usage

flexmix(formula, data = list(), k = NULL, cluster = NULL,
        model=NULL, control = NULL)
## S3 method for class 'flexmix':
summary(object, eps=1e-4, ...)

Arguments

formula

A symbolic description of the model to be fit. The general form is y~x|g where y is the response, x the set of predictors and g an optional grouping factor for repeated measurements.

data

An optional data frame containing the variables in the model.

k

Number of clusters (not needed if cluster is specified).

cluster

Factor or integer vector with the initial cluster assignments of observations at the start of the EM algorithm (default is random assignment into k clusters).

model

Object of FLXmodel of list of FLXmodel objects. Default is the object returned by calling FLXglm().

control

Object of class FLXcontrol or a named list.

object

Object of class flexmix.

eps

Probabilities below this treshold are treated as zero in the summary method.

...

Currently not used.

Value

• Returns an object of class flexmix.

Details

FlexMix models are described by objects of class FLXmodel, which in turn are created by driver functions like FLXglm or FLXmclust. Multivariate responses with independent components can be specified using a list of FLXmodel objects.

The summary method lists for each component the prior probability, the number of observations assigned to the corresponding cluster, the number of observations with a posterior probability larger than eps and the ratio of the latter two numbers (which indicates how separated the cluster is from the others).

Examples

data(NPreg)

## mixture of two linear regression models. Note that control parameters
## can be specified as named list and abbreviated if unique.
ex1 <- flexmix(yn~x+I(x^2), data=NPreg, k=2,
                   control=list(verb=5, iter=100))

ex1
summary(ex1)

## now we fit a model with one Gaussian response and one Poisson
## response. Note that the formulas inside the call to FLXglm are
## relative to the overall model formula.
ex2 <- flexmix(yn~x, data=NPreg, k=2,
               model=list(FLXglm(yn~.+I(x^2)), 
                          FLXglm(yp~., family="poisson")))

ex2
table(ex2@cluster, NPreg$class)

## for Gaussian responses we get coefficients and standard deviation
parameters(ex2, component=1, model=1)

## for Poisson response we get only coefficients
parameters(ex2, component=1, model=2)

## fitting a model only to the Poisson response is of course
## done like this
ex3 <- flexmix(yp~x, data=NPreg, k=2, model=FLXglm(family="poisson"))

## if observations are grouped, i.e., we have several observations per
## individual, fitting is usually much faster:
ex4 <- flexmix(yp~x|id1, data=NPreg, k=2, model=FLXglm(family="poisson"))