mixLoglikelihood_GLM

Log-likelihood of the multinomial logit.

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>.

Panagiotis Papastamoulis

multinomialLogitMix

Clustering Multinomial Count Data under the Presence of
Covariates

mixLoglikelihood_GLM function

<dl> <dt>y</dt>
<dd>matrix of counts</dd> <dt>theta</dt>
<dd>a three-dimensional array containing the multinomial probabilities per cluster, for each observation.</dd> <dt>pi</dt>
<dd>a numeric vector of length K (the number of mixture components) containing the mixing proportions.</dd></dl>

Arguments

Author

Log-likelihood of the multinomial logit. — mixLoglikelihood_GLM

<dl>

 <dt>y</dt>
<dd>matrix of counts</dd>

 <dt>theta</dt>
<dd>a three-dimensional array containing the multinomial probabilities per cluster, for each observation.</dd>

 <dt>pi</dt>
<dd>a numeric vector of length K (the number of mixture components) containing the mixing proportions.</dd>

</dl>

mixLoglikelihood_GLM: Log-likelihood of the multinomial logit.

Description

Usage

Value

Arguments

Author