R: Refit a Fitted Model

refit-methods {flexmix} | R Documentation

## Refit a Fitted Model

### Description

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

### Usage

    ## 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. doi:10.18637/jss.v011.i08

### Examples

    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)

[Package flexmix version 2.3-14 Index]