refit {flexmix} | R Documentation |
Refits an estimated flexmix model to obtain additional information like coefficient significance p-values for GLM regression.
## S4 method for signature 'flexmix, ANY': refit(object, newdata, model=1, which = c("model", "concomitant"), summary=TRUE, ...)
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 |
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.
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.
Friedrich Leisch
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/
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)