mixtools (version 0.1.0)

multmixmodel.sel: 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

A matrix of multinomial counts. An nxp matrix, where n is the sample size and p is the number of bins.

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.

...

Additional arguments passed to multmixEM.

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.

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