flexmix (version 2.0-1)

refit: Refit a Fitted Model

Description

Refits an estimated flexmix model to obtain additional information like coefficient significance p-values for GLM regression.

Usage

## S3 method for class 'flexmix,ANY':
refit(object, newdata, model=1, which = c("model",
"concomitant"), summary=TRUE, ...)

Arguments

object

An object of class "flexmix"

newdata

Optional new data.

model

The model (for a multivariate response) that shall be refitted.

which

Specifies if a component specific model or the concomitant variable model is refitted.

summary

A logical if the summary output should also be calculated.

...

Currently not used

Warning

The standard deviations are determined separately for each of the components using the a-posteriori probabilities as weights without accounting for the fact that the components have been simultaneously estimated. The derived standard deviations are hence approximative and should only be used in an exploratory way, as they are underestimating the uncertainty given that the missing information of the component memberships are replaced by the expected values.

The newdata argument can only be specified for refitting FLXMRglm components. A variant of glm for weighted ML estimation is used for fitting the components and full glm objects are returned. Please note that in this case the data and the model frame are stored for each component which can significantly increase the object size.

Details

The refit method for FLXMRglm models in combination with the summary method can be used to obtain the usual tests for significance of coefficients. Note that the tests are valid only if flexmix returned the maximum likelihood estimator of the parameters. For this method the returned object contains a glm object for each component where the elements model which is the model frame and data which contains the original dataset are missing.

References

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

Examples

data("NPreg")
ex1 <- flexmix(yn~x+I(x^2), data=NPreg, k=2)
ex1r <- refit(ex1)

## in one component all coefficients should be highly significant,
## in the other component only the linear term
summary(ex1r)