multmixmodel.sel {mixtools} | R Documentation |
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).
multmixmodel.sel(y, comps = NULL, ...)
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 . |
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).
compCDF
, makemultdata
, multmixEM
##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), epsilon = 1e-03)