Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/PLMIXfunctions.R
Perform MAP estimation via EM algorithm for a Bayesian mixture of Plackett-Luce models fitted to partial orderings.
1 2 3 4 |
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 |
Maximum number of EM iterations. |
hyper |
List of named objects with hyperparameter values for the conjugate prior specification: |
eps |
Tolerance value for the convergence criterion. |
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 |
plot_objective |
Logical: whether the objective function (that is the kernel of the log-posterior distribution) should be plotted. Default is |
Under noninformative (flat) prior setting, the EM algorithm for MAP estimation corresponds to the EMM algorithm described by Gormley and Murphy (2006) to perform frequentist inference. In this case, the MAP solution coincides with the MLE and the output vectors log_lik
and objective
coincide as well.
The mapPLMIX
function performs the MAP procedure with a single starting value. To address the issue of local maxima in the posterior distribution, see the mapPLMIX_multistart
function.
A list of S3 class mpPLMIX
with named elements:
|
Numeric vector with the MAP estimates of the G mixture weights. |
|
Numeric GxK matrix with the MAP estimates of the component-specific support parameters. |
|
Numeric NxG matrix of estimated posterior component membership probabilities. |
|
Numeric vector of N mixture component memberships based on MAP allocation from the |
|
Numeric vector of the log-likelihood values at each iteration. |
|
Numeric vector of the objective function values (that is the kernel of the log-posterior distribution) at each iteration. |
|
Maximized objective function value. |
|
BIC value (only for the default flat priors, otherwise |
|
Binary convergence indicator: 1 = convergence has been achieved, 0 = otherwise. |
|
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.
Gormley, I. C. and Murphy, T. B. (2006). Analysis of Irish third-level college applications data. Journal of the Royal Statistical Society: Series A, 169(2), pages 361–379, ISSN: 0964-1998, DOI: 10.1111/j.1467-985X.2006.00412.x.
1 2 3 4 5 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.