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 |
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 |
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)