R: Run FlexMix Repeatedly

stepFlexmix {flexmix} | R Documentation

## Run FlexMix Repeatedly

### Description

Runs flexmix repeatedly for different numbers of components and returns the maximum likelihood solution for each.

### Usage

    initFlexmix(..., k, init = list(), control = list(), nrep = 3L,
                verbose = TRUE, drop = TRUE, unique = FALSE)
    initMethod(name = c("tol.em", "cem.em", "sem.em"),
               step1 = list(tolerance = 10^-2),
               step2 = list(), control = list(), nrep = 3L)

    stepFlexmix(..., k = NULL, nrep = 3, verbose = TRUE, drop = TRUE,
                unique = FALSE)

    ## S4 method for signature 'stepFlexmix,missing'
    plot(x, y, what = c("AIC", "BIC", "ICL"),
      xlab = NULL, ylab = NULL, legend = "topright", ...)

    ## S4 method for signature 'stepFlexmix'
    getModel(object, which = "BIC")

    ## S4 method for signature 'stepFlexmix'
    unique(x, incomparables = FALSE, ...)

### Arguments

`...` | Passed to `flexmix` (or `matplot` in the `plot` method).
`k` | A vector of integers passed in turn to the `k` argument of `flexmix`.
`init` | An object of class `"initMethod"` or a named list where `initMethod` is called with it as arguments in addition to the `control` argument.
`name` | A character string indication which initialization strategy should be employed: short runs of EM followed by a long (`"tol.em"`), short runs of CEM followed by a long EM run (`"cem.em"`), short runs of SEM followed by a long EM run (`"sem.em"`).
`step1` | A named list which combined with the `control` argument is coercable to a `"FLXcontrol"` object. This control setting is used for the short runs.
`step2` | A named list which combined with the `control` argument is coercable to a `"FLXcontrol"` object. This control setting is used for the long run.
`control` | A named list which combined with the `step1` or the `step2` argument is coercable to a `"FLXcontrol"` object.
`nrep` | For each value of `k` run `flexmix` `nrep` times and keep only the solution with maximum likelihood. If `nrep` is set for the long run, it is ignored, because the EM algorithm is deterministic using the best solution discovered in the short runs for initialization.
`verbose` | If `TRUE`, show progress information during computations.
`drop` | If `TRUE` and `k` is of length 1, then a single flexmix object is returned instead of a `"stepFlexmix"` object.
`unique` | If `TRUE`, then `unique()` is called on the result, see below.
`x, object` | An object of class `"stepFlexmix"`.
`y` | Not used.
`what` | Character vector naming information criteria to plot. Functions of the same name must exist, which take a `stepFlexmix` object as input and return a numeric vector like `AIC,stepFlexmix-method` (see examples below).
`xlab,ylab` | Graphical parameters.
`legend` | If not `FALSE` and `what` contains more than 1 element, a legend is placed at the specified location, see `legend` for details.
`which` | Number of model to get. If character, interpreted as number of components or name of an information criterion.
`incomparables` | A vector of values that cannot be compared. Currently, `FALSE` is the only possible value, meaning that all values can be compared.

### Value

An object of class `"stepFlexmix"` containing the best models with respect to the log likelihood for the different number of components in a slot if `length(k)>1`, else directly an object of class `"flexmix"`.

If `unique = FALSE`, then the resulting object contains one model per element of `k` (which is the number of clusters the EM algorithm started with). If `unique = TRUE`, then the result is resorted according to the number of clusters contained in the fitted models (which may be less than the number with which the EM algorithm started), and only the maximum likelihood solution for each number of fitted clusters is kept. This operation can also be done manually by calling `unique()` on objects of class `"stepFlexmix"`.

### 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

Christophe Biernacki, Gilles Celeux and Gerard Govaert. Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models. Computational Statistics & Data Analysis, 41(3–4), 561–575, 2003.

Theresa Scharl, Bettina Gruen and Friedrch Leisch. Mixtures of regression models for time-course gene expression data: Evaluation of initialization and random effects. Bioinformatics, 26(3), 370–377, 2010.

### Examples

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

    ## try 2 times for k = 4
    set.seed(511)
    ex1 <- initFlexmix(Nclus~1, k = 4, model = FLXMCmvnorm(diagonal = FALSE),
                       nrep = 2)
    ex1

    ## now 2 times each for k = 2:5, specify control parameter
    ex2 <- initFlexmix(Nclus~1, k = 2:5, model = FLXMCmvnorm(diagonal = FALSE),
                       control = list(minprior = 0), nrep = 2)
    ex2
    plot(ex2)

    ## get BIC values
    BIC(ex2)

    ## get smallest model
    getModel(ex2, which = 1)

    ## get model with 3 components
    getModel(ex2, which = "3")

    ## get model with smallest ICL (here same as for AIC and BIC: true k = 4)
    getModel(ex2, which = "ICL")

    ## now 1 time each for k = 2:5, with larger minimum prior
    ex3 <- initFlexmix(Nclus~1, k = 2:5,
                       model = FLXMCmvnorm(diagonal = FALSE),
                       control = list(minprior = 0.1), nrep = 1)
    ex3

    ## keep only maximum likelihood solution for each unique number of
    ## fitted clusters:
    unique(ex3)

[Package flexmix version 2.3-14 Index]