stepmix 0.0.3

A Python package for multi-step estimation of latent class models with measurement and structural components. The package can also be used to fit mixture models with various observed random variables.

StepMix

This project is currently in development and a stable release is expected sometime during summer 2022.

A Python package for multi-step estimation of latent class models with measurement and structural components. The package can also be used to fit mixture models with various observed random variables. Largely based on Bakk & Kuha, 2018.

Install

You can install this repo directly with pip, preferably in a virtual environment :

pip install --upgrade git+https://github.com/Labo-Lacourse/stepmix.git

Usage

A simple example for 3-step estimation on simulated data :

from stepmix.datasets import data_bakk_response
from stepmix.stepmix import StepMix

# Soft 3-step 
X, Y, _ = data_bakk_response(n_samples=1000, sep_level=.7, random_state=42)
model = StepMix(n_components=3, n_steps=3, measurement='bernoulli', structural='gaussian_unit', assignment='soft',
            random_state=42)
model.fit(X, Y)
print(model.score(X, Y))  # Average log-likelihood

# Equivalently, each step can be performed individually. See the code of the fit method for details.
model = StepMix(n_components=3, measurement='bernoulli', structural='gaussian_unit', random_state=42)
model.em(X)  # Step 1
probs = model.predict_proba(X)  # Step 2
model.m_step_structural(probs, Y)  # Step 3
print(model.score(X, Y))

1-step and 2-step estimation are simply a matter of changing of the n_steps argument. Additionally, some bias correction methods are available for 3-step estimation.

References

• Bolck, A., Croon, M., and Hagenaars, J. Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political analysis, 12(1): 3–27, 2004.

• Vermunt, J. K. Latent class modeling with covariates: Two improved three-step approaches. Political analysis, 18 (4):450–469, 2010.

• Bakk, Z., Tekle, F. B., and Vermunt, J. K. Estimating the association between latent class membership and external variables using bias-adjusted three-step approaches. Sociological Methodology, 43(1):272–311, 2013.

• Bakk, Z. and Kuha, J. Two-step estimation of models between latent classes and external variables. Psychometrika, 83(4):871–892, 2018