Compute conditional predictive posterior \(p\)-values based on top paired comparison frequencies to assess the goodness-of-fit of a Bayesian mixtures of Plackett-Luce models for partial orderings.
ppcheckPLMIX_cond_single(
pi_inv,
G,
MCMCsampleP,
MCMCsampleW,
top1 = TRUE,
paired = TRUE
)A list of named objects:
post_pred_pvalue_top1If top1 is TRUE, posterior predictive \(p\)-value based on top frequencies, otherwise NULL.
post_pred_pvalue_pairedIf paired is TRUE, posterior predictive \(p\)-value based on paired comparison frequencies, otherwise NULL.
An object of class top_ordering, collecting the numeric \(N\)\(\times\)\(K\) data matrix of partial orderings, or an object that can be coerced with as.top_ordering.
Number of mixture components.
Numeric \(L\)\(\times\)\(G*K\) matrix with the MCMC samples of the component-specific support parameters.
Numeric \(L\)\(\times\)\(G\) matrix with the MCMC samples of the mixture weights.
Logical: whether the posterior predictive \(p\)-value based on top frequencies has to be computed. Default is TRUE.
Logical: whether the posterior predictive \(p\)-value based on paired comparison frequencies has to be computed. Default is TRUE.
Cristina Mollica and Luca Tardella
In the case of partial orderings, the same missingness patterns of the observed dataset, i.e., the number of items ranked by each sample unit, are reproduced on the replicated datasets.