R: Model Selection Mixtures of Multinomials

multmixmodel.sel {mixtools} | R Documentation

## Model Selection Mixtures of Multinomials

### Description

Assess the number of components in a mixture of multinomials model using the Akaike's information criterion (AIC), Schwartz's Bayesian information criterion (BIC), Bozdogan's consistent AIC (CAIC), and Integrated Completed Likelihood (ICL).

### Usage

    multmixmodel.sel(y, comps = NULL, ...)

### Arguments

`y` | Either An nxp matrix of data (multinomial counts), where n is the sample size and p is the number of multinomial bins, or the output of the `makemultdata` function. It is not necessary that all of the rows contain the same number of multinomial trials (i.e., the row sums of `y` need not be identical).
`comps` | Vector containing the numbers of components to consider. If NULL, this is set to be 1:(max possible), where (max possible) is floor((m+1)/2) and m is the minimum row sum of y.
`...` | Arguments passed to `multmixEM` that control convergence of the underlying EM algorithm.

### Value

`multmixmodel.sel` returns a table summarizing the AIC, BIC, CAIC, ICL, and log-likelihood values along with the winner (the number with the lowest aforementioned values).

### See Also

`compCDF`, `makemultdata`, `multmixEM`

### Examples

    ##Data generated using the multinomial cutpoint method.

    set.seed(100)
    x <- matrix(rpois(70, 6), 10, 7)
    x.new <- makemultdata(x, cuts = 5)
    multmixmodel.sel(x.new$y, comps = c(1,2), epsilon = 1e-03)

[Package mixtools version 1.2.0 Index]