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poLCA: An R Package for Polytomous Variable Latent Class Analysis
Open accessAbstract
poLCA is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment. Both models can be called using a single simple command line. The basic latent class model is a finite mixture model in which the component distributions are assumed to be multi-way cross-classification tables with all variables mutually independent. The latent class regression model further enables the researcher to estimate the effects of covariates on predicting latent class membership. poLCA uses expectation-maximization and Newton-Raphson algorithms to find maximum likelihood estimates of the model parameters.
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Cites
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Roesch, S. C., Villodas, M., & Villodas, F. (2010) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2010.01.003 · Cited by 148
Bishop-Fitzpatrick, L., Hong, J., Smith, L. E., Makuch, R. A., Greenberg, J. S., & Mailick, M. R. (2016) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-016-2816-3 · Cited by 142
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Metadata
- Authors
- Linzer, D. A., & Lewis, J. B.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2011
- Volume
- 42
- Issue
- 10
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W2171814052
- Cited by
- 1,636
- References
- 28
- FWCI
- 40.75
- Type
- journal article
- Language
- en
- Open access
- diamond
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
