Bayesian clustering for row effects models

We deal with two-way contingency tables having ordered column categories. We use a row effects model wherein each interaction term is assumed to have a multiplicative form involving a row effect parameter and a fixed column score. We propose a methodology to cluster row effects in order to simplify the interaction structure and enhancing the interpretation of the model. Our method uses a product partition model with a suitable specification of the cohesion function, so that we can carry out our analysis on a collection of models of varying dimensions using a straightforward MCMC sampler. The methodology is illustrated with reference to simulated and real data sets. -- Clustering ; Contingency table ; Log-linear model ; Markov Chain Monte Carlo ; Mixture of Dirichlet process prior ; Partition ; Product partition model ; Row effects model