multilevLCA: An R Package for Single-Level and Multilevel Latent Class Analysis with Covariates
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910404083449856 |
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| author | Lyrvall, Johan Di Mari, Roberto Bakk, Zsuzsa Oser, Jennifer Kuha, Jouni |
| author_facet | Lyrvall, Johan Di Mari, Roberto Bakk, Zsuzsa Oser, Jennifer Kuha, Jouni |
| contents | This contribution presents a guide to the R package multilevLCA, which offers a complete and innovative set of technical tools for the latent class analysis of single-level and multilevel categorical data. We describe the available model specifications, mainly falling within the fixed-effect or random-effect approaches. Maximum likelihood estimation of the model parameters, enhanced by a refined initialization strategy, is implemented either simultaneously, i.e., in one-step, or by means of the more advantageous two-step estimator. The package features i) semi-automatic model selection when a priori information on the number of classes is lacking, ii) predictors of class membership, and iii) output visualization tools for any of the available model specifications. All functionalities are illustrated by means of a real application on citizenship norms data, which are available in the package. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_07276 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | multilevLCA: An R Package for Single-Level and Multilevel Latent Class Analysis with Covariates Lyrvall, Johan Di Mari, Roberto Bakk, Zsuzsa Oser, Jennifer Kuha, Jouni Computation Methodology This contribution presents a guide to the R package multilevLCA, which offers a complete and innovative set of technical tools for the latent class analysis of single-level and multilevel categorical data. We describe the available model specifications, mainly falling within the fixed-effect or random-effect approaches. Maximum likelihood estimation of the model parameters, enhanced by a refined initialization strategy, is implemented either simultaneously, i.e., in one-step, or by means of the more advantageous two-step estimator. The package features i) semi-automatic model selection when a priori information on the number of classes is lacking, ii) predictors of class membership, and iii) output visualization tools for any of the available model specifications. All functionalities are illustrated by means of a real application on citizenship norms data, which are available in the package. |
| title | multilevLCA: An R Package for Single-Level and Multilevel Latent Class Analysis with Covariates |
| topic | Computation Methodology |
| url | https://arxiv.org/abs/2305.07276 |