Refit a Fitted Model

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Description

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

Usage

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## S4 method for signature 'flexmix'
refit(object, newdata, method = c("optim",
"mstep"), ...)
## S4 method for signature 'FLXRoptim'
summary(object, model = 1, which = c("model",
"concomitant"), ...)
## S4 method for signature 'FLXRmstep'
summary(object, model = 1, which = c("model",
"concomitant"), ...)

## S4 method for signature 'FLXRoptim,missing'
plot(x, y, model = 1, which = c("model", "concomitant"),
         bycluster = TRUE, alpha = 0.05, components, labels = NULL,
         significance = FALSE, xlab = NULL, ylab = NULL, ci = TRUE,
         scales = list(), as.table = TRUE, horizontal = TRUE, ...)

Arguments

object

An object of class "flexmix"

newdata

Optional new data.

method

Specifies if the variance covariance matrix is determined using optim or if the posteriors are assumed as given and an M-step is performed.

model

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

which

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

x

An object of class "FLXRoptim"

y

Missing object.

bycluster

A logical if the parameters should be group by cluster or by variable.

alpha

Numeric indicating the significance level.

components

Numeric vector specifying which components are plotted. The default is to plot all components.

labels

Character vector specifying the variable names used.

significance

A logical indicating if non-significant coefficients are shaded in a lighter grey.

xlab

String for the x-axis label.

ylab

String for the y-axis label.

ci

A logical indicating if significant and insignificant parameter estimates are shaded differently.

scales

See argument of the same name for function xyplot.

as.table

See arguments of the same name for function xyplot.

horizontal

See arguments of the same name for function xyplot.

...

Currently not used

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. If refit is used with method = "mstep" for these component specific models 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.

Value

An object inheriting form class FLXR is returned. For the method using optim the object has class FLXRoptim and for the M-step method it has class FLXRmstep. Both classes give similar results for their summary methods. Objects of class FLXRoptim have their own plot method. Lapply can be used to further analyse the refitted component specific models of objects of class FLXRmstep.

Warning

For method = "mstep" 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 when using method = "mstep" 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.

Author(s)

Friedrich Leisch and Bettina Gruen

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

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data("NPreg", package = "flexmix")
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)

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