# cube000: Main function for CUBE models without covariates In CUB: A Class of Mixture Models for Ordinal Data

## Description

Estimate and validate a CUBE model without covariates.

## Usage

 `1` ```cube000(m, ordinal, starting, maxiter, toler, expinform) ```

## Arguments

 `m` Number of ordinal categories `ordinal` Vector of ordinal responses `starting` Vector of initial estimates to start the optimization algorithm, whose length equals the number of parameters of the model `maxiter` Maximum number of iterations allowed for running the optimization algorithm `toler` Fixed error tolerance for final estimates `expinform` Logical: if TRUE, the function returns the expected variance-covariance matrix

## Value

An object of the class "CUBE"

## References

Iannario, M. (2014). Modelling Uncertainty and Overdispersion in Ordinal Data, Communications in Statistics - Theory and Methods, 43, 771–786
Iannario, M. (2015). Detecting latent components in ordinal data with overdispersion by means of a mixture distribution, Quality & Quantity, 49, 977–987

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26``` ```### Applying donttest option since the proposed examples require long time run for check data(relgoods) m=10 ordinal=na.omit(relgoods[,37]) starting = rep(0.1, 3) fitcube=cube000(m, ordinal, starting, maxiter=500, toler=1e-6, makeplot=TRUE, expinform=FALSE, summary=T) param=fitcube\$estimates pai=param[1] # ML estimate for the uncertainty parameter csi=param[2] # ML estimate for the feeling parameter phi=param[3] # ML estimate for the overdispersion parameter maxlik=fitcube\$loglik niter=fitcube\$niter BIC=fitcube\$BIC ################### data(univer) m=7 ordinal=univer[,8] starting=inibestcube(m,ordinal) model=cube000(m,ordinal,starting,maxiter=200,toler=1e-4,makeplot=TRUE,expinform=TRUE,summary=F) param=model\$estimates # Final ML estimates (pai,csi,phi) maxlik=model\$loglik model\$varmat model\$niter model\$BIC ```

CUB documentation built on Feb. 9, 2018, 6:14 a.m.