Perform a best-subset search for CUB models on the basis of the BIC index, by combining all possible covariates' specification for feeling and for uncertainty parameters
bestcub(ordinal,m,Y,W,toler=1e-4,maxiter=200,iterc=5,alpha=0.05,mix=FALSE,
tolmix=1e+2,fmix=NULL,invgen=TRUE)
A list containing the following results:
List of all estimated models (with the accelerated EM)
Names of covariates for feeling in the best model with all significant effect
Names of covariates for feeling in the best model with all significant effect
ML estimates of the best model
Estimated standard errors for the best model
BIC index of the best (significant) model
Matrix of computational time for each of the estimated model
Matrix of number of iterations occurred for each of the estimated model
Vector of ordinal responses
Number of ordinal categories
Matrix of selected covariates for the uncertainty parameter
Matrix of selected covariates for the feeling parameter
Fixed error tolerance for final estimates
Maximum number of iterations allowed for running the optimization algorithm
Iteration from which the acceleration strategy starts
Significant level for Wald test
Logical: should a first preliminary standard EM be run at toler equal to tolmix? (default is FALSE)
Error tolerance for first preliminary EM (if mix=TRUE).
Fraction of iteration needed for first preliminary EM (if mix=TRUE). Default is null.
Logical: should the recursive formula for the inverse of the information matrix be considered? (Default is TRUE)
fastCUB