multinomialLogitMix-package: Clustering Multinomial Count Data under the Presence of...

In multinomialLogitMix: Clustering Multinomial Count Data under the Presence of Covariates

Clustering Multinomial Count Data under the Presence of Covariates

Description

Methods for model-based clustering of multinomial counts under the presence of covariates using mixtures of multinomial logit models, as implemented in Papastamoulis (2023) <DOI:10.1007/s11634-023-00547-5>. These models are estimated under a frequentist as well as a Bayesian setup using the Expectation-Maximization algorithm and Markov chain Monte Carlo sampling (MCMC), respectively. The (unknown) number of clusters is selected according to the Integrated Completed Likelihood criterion (for the frequentist model), and estimating the number of non-empty components using overfitting mixture models after imposing suitable sparse prior assumptions on the mixing proportions (in the Bayesian case), see Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>. In the latter case, various MCMC chains run in parallel and are allowed to switch states. The final MCMC output is suitably post-processed in order to undo label switching using the Equivalence Classes Representatives (ECR) algorithm, as described in Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>.

Details

The DESCRIPTION file: This package was not yet installed at build time.
Index: This package was not yet installed at build time.
See the main function of the package: multinomialLogitMix, which wraps automatically calls to the MCMC sampler gibbs_mala_sampler_ppt and the EM algorithm mix_mnm_logistic.

Author(s)

Panagiotis Papastamoulis [aut, cre] (<https://orcid.org/0000-0001-9468-7613>)

Maintainer: Panagiotis Papastamoulis <[e-mail]>

References

Papastamoulis, P. Model based clustering of multinomial count data. Advances in Data Analysis and Classification (2023). https://doi.org/10.1007/s11634-023-00547-5

Papastamoulis, P. and Iliopoulos, G. (2010). An Artificial Allocations Based Solution to the Label Switching Problem in Bayesian Analysis of Mixtures of Distributions. Journal of Computational and Graphical Statistics, 19(2), 313-331. http://www.jstor.org/stable/25703571

Papastamoulis, P. (2016). label.switching: An R Package for Dealing with the Label Switching Problem in MCMC Outputs. Journal of Statistical Software, Code Snippets, 69(1), 1-24. https://doi.org/10.18637/jss.v069.c01

Rousseau, J. and Mengersen, K. (2011), Asymptotic behaviour of the posterior distribution in overfitted mixture models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73: 689-710. https://doi.org/10.1111/j.1467-9868.2011.00781.x

See Also

multinomialLogitMix, gibbs_mala_sampler_ppt,mix_mnm_logistic

multinomialLogitMix documentation built on July 26, 2023, 6:07 p.m.