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Labo-Lacourse / stepmix

A Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods.
https://stepmix.readthedocs.io/en/latest/index.html
MIT License
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is there any solution when there are many observations per respondent #49

Closed peter159 closed 7 months ago

peter159 commented 11 months ago

like the question titled, and when I want to segment respondent applying this stepmix package

sachaMorin commented 11 months ago

If you're looking for something to handle temporal data (e.g., latent growth modeling), this is not yet supported by StepMix unfortunately.

peter159 commented 11 months ago

thank you

sachaMorin commented 11 months ago

@ericlacourse pointed out that you could consider RMLCA which can be achieved (I think) with StepMix. You would need to structure your n respondents with k repeated measurements as an n x k data frame and use this as input to StepMix.

sachaMorin commented 7 months ago

Closing. Feel free to reopen if needed.