Description Usage Arguments Details Value Author(s) References Examples
View source: R/PLMIXfunctions.R
Perform Gibbs sampling simulation for a Bayesian mixture of Plackett-Luce models fitted to partial orderings.
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pi_inv |
An object of class |
K |
Number of possible items. |
G |
Number of mixture components. |
init |
List of named objects with initialization values: |
n_iter |
Total number of MCMC iterations. |
n_burn |
Number of initial burn-in drawings removed from the returned MCMC sample. |
hyper |
List of named objects with hyperparameter values for the conjugate prior specification: |
centered_start |
Logical: whether a random start whose support parameters and weights should be centered around the observed relative frequency that each item has been ranked top. Default is |
The size L of the final MCMC sample is equal to n_iter
-n_burn
.
A list of S3 class gsPLMIX
with named elements:
|
Numeric LxG matrix with MCMC samples of the mixture weights. |
|
Numeric Lx(G*K) matrix with MCMC samples of the component-specific support parameters. |
|
Numeric vector of L posterior log-likelihood values. |
|
Numeric vector of L posterior deviance values (-2 * |
|
Numeric vector of L objective function values (that is the kernel of the log-posterior distribution). |
|
The matched call. |
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.
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