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

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  • Meulders, Michel

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.

Suggested Citation

  • Meulders, Michel, 2013. "An R Package for Probabilistic Latent Feature Analysis of Two-Way Two-Mode Frequencies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i14).
    • Handle: RePEc:jss:jstsof:v:054:i14
    DOI: http://hdl.handle.net/10.18637/jss.v054.i14
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    References listed on IDEAS

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    1. Torres, Anna & Bijmolt, Tammo H.A., 2009. "Assessing brand image through communalities and asymmetries in brand-to-attribute and attribute-to-brand associations," European Journal of Operational Research, Elsevier, vol. 195(2), pages 628-640, June.
    2. Vermunt, Jeroen K., 2007. "A hierarchical mixture model for clustering three-way data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5368-5376, July.
    3. Andrew Gelman & Iven Van Mechelen & Geert Verbeke & Daniel F. Heitjan & Michel Meulders, 2005. "Multiple Imputation for Model Checking: Completed-Data Plots with Missing and Latent Data," Biometrics, The International Biometric Society, vol. 61(1), pages 74-85, March.
    4. Michel Meulders & Paul Boeck & Iven Mechelen, 2003. "A taxonomy of latent structure assumptions for probability matrix decomposition models," Psychometrika, Springer;The Psychometric Society, vol. 68(1), pages 61-77, March.
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