StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909224874803200 |
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| author | Morin, Sacha Legault, Robin Laliberté, Félix Bakk, Zsuzsa Giguère, Charles-Édouard de la Sablonnière, Roxane Lacourse, Éric |
| author_facet | Morin, Sacha Legault, Robin Laliberté, Félix Bakk, Zsuzsa Giguère, Charles-Édouard de la Sablonnière, Roxane Lacourse, Éric |
| contents | StepMix is an open-source Python package for the pseudo-likelihood estimation (one-, two- and three-step approaches) of generalized finite mixture models (latent profile and latent class analysis) with external variables (covariates and distal outcomes). In many applications in social sciences, the main objective is not only to cluster individuals into latent classes, but also to use these classes to develop more complex statistical models. These models generally divide into a measurement model that relates the latent classes to observed indicators, and a structural model that relates covariates and outcome variables to the latent classes. The measurement and structural models can be estimated jointly using the so-called one-step approach or sequentially using stepwise methods, which present significant advantages for practitioners regarding the interpretability of the estimated latent classes. In addition to the one-step approach, StepMix implements the most important stepwise estimation methods from the literature, including the bias-adjusted three-step methods with Bolk-Croon-Hagenaars and maximum likelihood corrections and the more recent two-step approach. These pseudo-likelihood estimators are presented in this paper under a unified framework as specific expectation-maximization subroutines. To facilitate and promote their adoption among the data science community, StepMix follows the object-oriented design of the scikit-learn library and provides an additional R wrapper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_03853 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables Morin, Sacha Legault, Robin Laliberté, Félix Bakk, Zsuzsa Giguère, Charles-Édouard de la Sablonnière, Roxane Lacourse, Éric Methodology Machine Learning StepMix is an open-source Python package for the pseudo-likelihood estimation (one-, two- and three-step approaches) of generalized finite mixture models (latent profile and latent class analysis) with external variables (covariates and distal outcomes). In many applications in social sciences, the main objective is not only to cluster individuals into latent classes, but also to use these classes to develop more complex statistical models. These models generally divide into a measurement model that relates the latent classes to observed indicators, and a structural model that relates covariates and outcome variables to the latent classes. The measurement and structural models can be estimated jointly using the so-called one-step approach or sequentially using stepwise methods, which present significant advantages for practitioners regarding the interpretability of the estimated latent classes. In addition to the one-step approach, StepMix implements the most important stepwise estimation methods from the literature, including the bias-adjusted three-step methods with Bolk-Croon-Hagenaars and maximum likelihood corrections and the more recent two-step approach. These pseudo-likelihood estimators are presented in this paper under a unified framework as specific expectation-maximization subroutines. To facilitate and promote their adoption among the data science community, StepMix follows the object-oriented design of the scikit-learn library and provides an additional R wrapper. |
| title | StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2304.03853 |