Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data
The analysis of binary three-way data (i.e., persons who indicate which attributes apply to each of a set of objects) may be of interest in several substantive domains as sensory profiling, marketing research or personality assessment. Latent class probabilistic latent feature models (LCPLFMs) may b...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2018-10-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/2533 |
| _version_ | 1819126001251123200 |
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| author | Michel Meulders Philippe De Bruecker |
| author_facet | Michel Meulders Philippe De Bruecker |
| author_sort | Michel Meulders |
| collection | DOAJ |
| description | The analysis of binary three-way data (i.e., persons who indicate which attributes apply to each of a set of objects) may be of interest in several substantive domains as sensory profiling, marketing research or personality assessment. Latent class probabilistic latent feature models (LCPLFMs) may be used to explain binary object-attribute associations on the basis of a small number of binary latent variables (called latent features). As LCPLFMs aim to model object-attribute associations using a small number of latent features they may be more suited to analyze data with many objects/attributes than standard multilevel latent class models which do not include such a dimension reduction. In this paper we describe new functions of the plfm package for analyzing binary three-way data with LCPLFMs. The new functions provide a flexible modeling approach as they allow to (1) specify different assumptions for modeling statistical dependencies between object-attribute pairs, (2) use different assumptions for modeling parameter heterogeneity across persons, (3) conduct a confirmatory analysis by constraining specific parameters to pre-specified values, (4) inspect results with print, summary and plot methods. As an illustration, the models are applied to analyze data on the perception of midsize cars, and to study the situational determinants of anger-related behavior. |
| first_indexed | 2024-12-22T07:49:05Z |
| format | Article |
| id | doaj.art-5e789aa096ba4a34afa97ebb33ce9e86 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-22T07:49:05Z |
| publishDate | 2018-10-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-5e789aa096ba4a34afa97ebb33ce9e862022-12-21T18:33:33ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602018-10-0187114510.18637/jss.v087.i011252Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary DataMichel MeuldersPhilippe De BrueckerThe analysis of binary three-way data (i.e., persons who indicate which attributes apply to each of a set of objects) may be of interest in several substantive domains as sensory profiling, marketing research or personality assessment. Latent class probabilistic latent feature models (LCPLFMs) may be used to explain binary object-attribute associations on the basis of a small number of binary latent variables (called latent features). As LCPLFMs aim to model object-attribute associations using a small number of latent features they may be more suited to analyze data with many objects/attributes than standard multilevel latent class models which do not include such a dimension reduction. In this paper we describe new functions of the plfm package for analyzing binary three-way data with LCPLFMs. The new functions provide a flexible modeling approach as they allow to (1) specify different assumptions for modeling statistical dependencies between object-attribute pairs, (2) use different assumptions for modeling parameter heterogeneity across persons, (3) conduct a confirmatory analysis by constraining specific parameters to pre-specified values, (4) inspect results with print, summary and plot methods. As an illustration, the models are applied to analyze data on the perception of midsize cars, and to study the situational determinants of anger-related behavior.https://www.jstatsoft.org/index.php/jss/article/view/2533latent featurethree-way datadisjunctive modelconjunctive modelperceptual mappingindividual differencesem algorithmr |
| spellingShingle | Michel Meulders Philippe De Bruecker Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data Journal of Statistical Software latent feature three-way data disjunctive model conjunctive model perceptual mapping individual differences em algorithm r |
| title | Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data |
| title_full | Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data |
| title_fullStr | Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data |
| title_full_unstemmed | Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data |
| title_short | Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data |
| title_sort | latent class probabilistic latent feature analysis of three way three mode binary data |
| topic | latent feature three-way data disjunctive model conjunctive model perceptual mapping individual differences em algorithm r |
| url | https://www.jstatsoft.org/index.php/jss/article/view/2533 |
| work_keys_str_mv | AT michelmeulders latentclassprobabilisticlatentfeatureanalysisofthreewaythreemodebinarydata AT philippedebruecker latentclassprobabilisticlatentfeatureanalysisofthreewaythreemodebinarydata |