An R Package for Probabilistic Latent Feature Analysis of Two-Way Two-Mode Frequencies
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- 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.
|Date of creation:||Sep 2012|
|Date of revision:|
|Contact details of provider:|| Web page: http://research.hubrussel.be|
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:hub:wpecon:201232. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sabine Janssens)
If references are entirely missing, you can add them using this form.