Pymer4: Generalized Linear & Multi-level Models in Python

Pymer4: Generalized Linear & Multi-level Models in Python#

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pymer4

A simple Python replacement for R’s lm(), glm(), lmer() and glmer()

pymer4 is a statistics library for estimating various regression models, multi-level models, and generalized-linear-mixed models in Python. Jealous of R’s lovely model syntax by prefer to work in the scientific Python ecoysystem? This package has got you covered! pymer4 provides a clean interface that hides the back-and-forth code required when moving between R and Python. This is accomplished using rpy2 to interface between languages.

Why?#

The scientific Python ecosystem has tons of fantastic libraries for data-analysis and statistical modeling such as statsmodels, pingouin, scikit-learn, and bambi for bayesian models to name a few. However, Python still sorely lacks a unified formula-based modeling interface that rivals what’s available in R. This makes it frustrating for analysts, students, and educators who typically work in Python, and requires jumping between different libraries and languages in order to accomplish a single task. Rather than completely reinventing the wheel, pymer4 aims to bring the best of R’s robust modeling capabilities to Python for the most common General(ized)-Linear-(Mixed)-Modeling (GLMMs) needs in the social and behavioral sciences.

At the same time, pymer4 includes numerous quality-of-life features for common tasks you’re likely to do when working with models (e.g. automatically calculated fit statistics, residuals, p-values for mixed-models, bootstrapped confidence-intervals, random-effects deviances, etc). By bringing together functionality spread across several popular R tools, we’ve aimed for intuitive-usability. pymer4 also notably builds on top of the polars Dataframe library rather than pandas. This keeps code simple, fast, and efficient, while opening the door for enhanced future functionality.

Publications#

pymer4 has been used to analyze data is several publications including but not limited to:

  • Jolly, E., Sadhukha, S., & Chang, L.J. (in press). Custom-molded headcases have limited efficacy in reducing head motion during naturalistic fMRI expreiments. NeuroImage.

  • Sharon, G., Cruz, N. J., Kang, D. W., et al. (2019). Human gut microbiota from autism spectrum disorder promote behavioral symptoms in mice. Cell, 177(6), 1600-1618.

  • Urbach, T. P., DeLong, K. A., Chan, W. H., & Kutas, M. (2020). An exploratory data analysis of word form prediction during word-by-word reading. Proceedings of the National Academy of Sciences, 117(34), 20483-20494.

  • Chen, P. H. A., Cheong, J. H., Jolly, E., Elhence, H., Wager, T. D., & Chang, L. J. (2019). Socially transmitted placebo effects. Nature Human Behaviour, 3(12), 1295-1305.

Citing#

If you use pymer4 in your own work, please cite:

Jolly, (2018). Pymer4: Connecting R and Python for Linear Mixed Modeling. Journal of Open Source Software, 3(31), 862, https://doi.org/10.21105/joss.00862

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