criteria: criteria estimation
In Rankcluster: Model-Based Clustering for Multivariate Partial Ranking Data

Description Usage Arguments Value Author(s) Examples

This function estimates the loglikelihood of a mixture of multidimensional ISR model, as well as the BIC and ICL model selection criteria.

1 2	criteria(data, proportion, pi, mu, m, Ql = 500, Bl = 100, IC = 1, nb_cpus = 1)

`data`	a matrix in which each row is a rank (partial or not; for partial rank, missing elements of a rank are put to 0 ).
`proportion`	a vector (which sums to 1) containing the K mixture proportions.
`pi`	a matrix of size K*p, where K is the number of clusters and p the number of dimension, containing the probabilities of a good comparaison of the model (dispersion parameters).
`mu`	a matrix of size K*sum(m), containing the modal ranks. Each row contains the modal rank for a cluster. In the case of multivariate ranks, the reference rank for each dimension are set successively on the same row.
`m`	a vector containing the size of ranks for each dimension.
`Ql`	number of iterations of the Gibbs sampler used for the estimation of the log-likelihood.
`Bl`	burn-in period of the Gibbs sampler.
`IC`	number of run of the computation of the loglikelihood.
`nb_cpus`	number of cpus for parallel computation

a list containing:

`ll`	the estimated log-likelihood.
`bic`	the estimated BIC criterion.
`icl`	the estimated ICL criterion.

Quentin Grimonprez

data(big4)
res = rankclust(big4$data, m = big4$m, K = 2, Ql = 100, Bl = 50, maxTry = 2)
if(res@convergence)
	crit = criteria(big4$data, res[2]@proportion, res[2]@pi, res[2]@mu, 
	                big4$m, Ql = 200, Bl = 100)