cubecov: Main function for CUBE models with covariates

In CUB: A Class of Mixture Models for Ordinal Data

Description

Function to estimate and validate a CUBE model with explicative covariates for all the three parameters.

Usage

cubecov(m, ordinal, Y, W, Z, starting, maxiter, toler)

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

Value

An object of the class "CUBE"

References

Piccolo, D. (2014). Inferential issues on CUBE models with covariates, Communications in Statistics - Theory and Methods, 44, DOI: 10.1080/03610926.2013.821487

CUB documentation built on March 31, 2020, 5:14 p.m.