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Cluster-weighted models using Stata
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DOI：10.1177/1536867X241297922.png)
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Abstract
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The cluster-weighted model (CWM) is a member of the family of mixtures of regression models and is also known as a mixture of regressions with random covariates. CWMs refer to the framework of model-based clustering and naturally apply when the research interest requires modeling the relationship between a response variable and a set of covariates using a regression-based approach such as a generalized linear model with the sample being suspected of comprising heterogeneous latent classes. A command for fitting these models is not yet available in Stata, so the aim of this article is to introduce the package cwmglm, which fits CWMs based on the most common generalized linear models with random covariates. Moreover, cwmglm allows the estimation of parsimonious models of Gaussian distributions, with the parameterization of the variance-covariance matrix based on the eigenvalue decomposition. These features are completely new for Stata users. The cwmglm package features goodness-of-fit, bootstrapping, and model-selection tools. We illustrate the use of cwmglm with real and simulated datasets.
Keywords:
st0762
cwmglm
cluster-weighted model
finite mixtures of regressions with random covariates
model-based clustering
saturated mixture regression model
Gaussian parsimonious models
postestimation
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