Journal of Statistical Software vol:54 issue:14 pages:1-29
A common strategy for the analysis of object-attribute associations is to derive a lowdimensional spatial representation of objects and attributes which involves a compensatory model (e.g., principal components analysis) to explain the strength of object-attribute associations. As an alternative, probabilistic latent feature models assume that objects and attributes can be represented as a set of binary latent features and that the strength of object-attribute associations can be explained as a non-compensatory (e.g., disjunctive or conjunctive) mapping of latent features. In this paper, we describe the R package plfm which comprises functions for conducting both classical and Bayesian probabilistic latent feature analysis with disjunctive or a conjunctive mapping rules. Print and summary functions are included to summarize results on parameter estimation, model selection
and the goodness of t of the models. As an example the functions of plfm are used to analyze product-attribute data on the perception of car models, and situation-behavior
associations on the situational determinants of anger-related behavior.