Usage
cubecov(m, ordinal, Y, W, Z, starting, maxiter, toler, summary)
Arguments
m
Number of ordinal categories
ordinal
Vector of ordinal responses
Y
Matrix of selected covariates for explaining the uncertainty component
W
Matrix of selected covariates for explaining the feeling component
Z
Matrix of selected covariates for explaining the overdispersion component
starting
Vector of initial parameters estimates to start the optimization algorithm
(it has length NCOL(Y) + NCOL(W) + NCOL(Z) + 3 to account for intercept terms
for all the three components
maxiter
Maximum number of iterations allowed for running the optimization algorithm
toler
Fixed error tolerance for final estimates
summary
Logical: if TRUE, summary results of the fitting procedure are displayed on screen