Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Michel Meulders
Title: An R Package for Probabilistic Latent Feature Analysis of Two-Way Two-Mode Frequencies
Abstract: A common strategy for the analysis of object-attribute associations is to derive a low- dimensional 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 fit 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.

Page views:: 2670. Submitted: 2011-12-19. Published: 2013-09-16.
Paper: An R Package for Probabilistic Latent Feature Analysis of Two-Way Two-Mode Frequencies     Download PDF (Downloads: 3540)
plfm_1.1.tar.gz: R source package Download (Downloads: 286; 35KB)
v54i14.R: R example code from the paper Download (Downloads: 307; 4KB)

DOI: 10.18637/jss.v054.i14

This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.