criteria {Rankcluster} | R Documentation |
Criteria estimation
Description
This function estimates the loglikelihood of a mixture of multidimensional ISR model, as well as the BIC and ICL model selection criteria.
Usage
criteria(data, proportion, pi, mu, m, Ql = 500, Bl = 100, IC = 1, nb_cpus = 1)
Arguments
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 comparison 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 |
Value
a list containing:
ll |
the estimated log-likelihood. |
bic |
the estimated BIC criterion. |
icl |
the estimated ICL criterion. |
Author(s)
Quentin Grimonprez
Examples
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)
}