An R Package for Probabilistic Latent Feature Analysis of Two-Way Two-Mode Frequencies

Michel Meulders

Journal of Statistical Software, 2013, vol. 054, issue i14

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.

Date: 2013-09-16
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/vi ... Mode_Frequencies.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... 4i14/plfm_1.1.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... ile/v054i14/v54i14.R

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:054:i14

DOI: 10.18637/jss.v054.i14

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum ().