Likelihood and log-likelihood evaluation for a mixture of Plackett-Luce models
Compute either the likelihood or the log-likelihood of the Plackett-Luce mixture model parameters for a partial ordering dataset.
likPLMIX(p, ref_order, weights, pi_inv) loglikPLMIX(p, ref_order, weights, pi_inv)
p |
Numeric GxK matrix of component-specific support parameters. |
ref_order |
Numeric GxK matrix of component-specific reference orders. |
weights |
Numeric vector of G mixture weights. |
pi_inv |
An object of class |
The ref_order argument accommodates for the more general mixture of Extended Plackett-Luce models (EPL), involving the additional reference order parameters (Mollica and Tardella 2014). A permutation of the first K integers can be specified in each row of the ref_order argument. Since the Plackett-Luce model is a special instance of the EPL with the reference order equal to the identity permutation, the ref_order argument must be a matrix with G rows equal to (1,…,K) when dealing with Plackett-Luce mixtures.
Either the likelihood or the log-likelihood value of the Plackett-Luce mixture model parameters for a partial ordering dataset.
Cristina Mollica and Luca Tardella
Mollica, C. and Tardella, L. (2017). Bayesian Plackett-Luce mixture models for partially ranked data. Psychometrika, 82(2), pages 442–458, ISSN: 0033-3123, DOI: 10.1007/s11336-016-9530-0.
Mollica, C. and Tardella, L. (2014). Epitope profiling via mixture modeling for ranked data. Statistics in Medicine, 33(21), pages 3738–3758, ISSN: 0277-6715, DOI: 10.1002/sim.6224.
data(d_apa)
K <- ncol(d_apa)
G <- 3
support_par <- matrix(1:(G*K), nrow=G, ncol=K)
weights_par <- c(0.50, 0.25, 0.25)
loglikPLMIX(p=support_par, ref_order=matrix(1:K, nrow=G, ncol=K, byrow=TRUE),
weights=weights_par, pi_inv=d_apa)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.