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 |
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 rowsums 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. |
... |
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)