StepMix

For StepMixR, please refer to this repository.

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 based on pseudolikelihood theory. Additional features include support for covariates and distal outcomes, various simulation utilities, and non-parametric bootstrapping, which allows inference in semi-supervised and unsupervised settings.

Reference

If you find StepMix useful, please consider citing our arXiv preprint:

@article{morin2023stepmix,
  title={StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables},
  author={Morin, Sacha and Legault, Robin and Bakk, Zsuzsa and Gigu{\`e}re, Charles-{\'E}douard and de la Sablonni{\`e}re, Roxane and Lacourse, {\'E}ric},
  journal={arXiv preprint arXiv:2304.03853},
  year={2023}
}

Install

You can install StepMix with pip, preferably in a virtual environment:

pip install stepmix

Quickstart

A simple StepMix mixture using the continuous variables of the Iris Dataset:

import pandas as pd
from sklearn.datasets import load_iris
from sklearn.metrics import rand_score

from stepmix.stepmix import StepMix

# Load dataset in a Dataframe
data_continuous, target = load_iris(return_X_y=True, as_frame=True)

# Continuous StepMix Model with 3 latent classes
model = StepMix(n_components=3, measurement="continuous", verbose=0, random_state=123)

# Fit model and predict clusters
model.fit(data_continuous)
pred_continuous = model.predict(data_continuous)

# A Rand score close to 1 indicates good alignment between clusters and flower types
print(rand_score(pred_continuous, target))

StepMix also provides support for categorical mixtures:

# Create categorical data based on the Iris Dataset quantiles
data_categorical = data_continuous.copy()
for col in data_categorical:
   data_categorical[col] = pd.qcut(data_continuous[col], q=3).cat.codes

# Categorical StepMix Model with 3 latent classes
model = StepMix(n_components=3, measurement="categorical", verbose=0, random_state=123)

# Fit model and predict clusters
model.fit(data_categorical)
pred_categorical = model.predict(data_categorical)

# A Rand score close to 1 indicates good alignment between clusters and flower types
print(rand_score(pred_categorical, target))

Please refer to the StepMix tutorials to learn how to handle missing values and combine continuous and categorical data in the same model.

Tutorials

Detailed tutorials are available in notebooks:

1. Generalized Mixture Models with StepMix: an in-depth look at how latent class models can be defined with StepMix. The tutorial uses the Iris Dataset as an example and covers:
   1. Continuous LCA models (latent profile analysis/gaussian mixture model);
   2. Binary LCA models;
   3. Categorical LCA models;
   4. Mixed variables mixture models (continuous and categorical data);
   5. Missing Values through Full-Information Maximum Likelihood.
2. Stepwise Estimation with StepMix: a tutorial demonstrating how to define measurement and structural models. The tutorial discusses:
   1. LCA models with distal outcomes;
   2. LCA models with covariates;
   3. 1-step, 2-step and 3-step estimation;
   4. Corrections (BCH or ML) and other options for 3-step estimation.
3. Model Selection: a short tutorial discussing:
   1. Selecting the number of components in a mixture model (n_components);
   2. Comparing models with fit indices: AIC and BIC.
4. Parameters, Bootstrapping and CI: a tutorial discussing how to:
   1. Access StepMix parameters;
   2. Bootstrap StepMix estimators;
   3. Quickly plot confidence intervals.