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Estimating marginal effects with zero-inflated models: A tutorial with the R package mzim.

Chendong Li1, Oi-Man Kwok2, Timothy Lawrence2

  • 1Department of Educational Psychology, Texas A&M University, Harrington Tower, College Station, TX, 77843-4225, USA. cliattx@tamu.edu.

Behavior Research Methods
|May 22, 2026
PubMed
Summary
This summary is machine-generated.

Marginalized zero-inflated (mZI) models offer interpretable population-averaged effects for count data with excess zeros. The new R package, mzim, makes these powerful marginalized zero-inflated Poisson (mZIP) and Negative Binomial (mZINB) models accessible for researchers.

Keywords:
Count DataMarginalized Zero-inflated ModelR PackageZero-inflation

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Area of Science:

  • Psychological and Health Sciences
  • Biostatistics
  • Statistical Modeling

Background:

  • Count data in psychological and health sciences frequently exhibit excess zeros (zero inflation).
  • Traditional zero-inflated models (ZIP, ZINB) present interpretation challenges and may yield biased estimates for population-level effects.
  • Applied researchers need accessible tools for analyzing zero-inflated count data effectively.

Purpose of the Study:

  • To provide a tutorial on the theory, estimation, and interpretation of marginalized zero-inflated (mZI) models.
  • To introduce mzim, a novel R package for implementing marginalized zero-inflated Poisson (mZIP) and Negative Binomial (mZINB) models.
  • To demonstrate the practical application and benefits of mZI models using real-world data.

Main Methods:

  • Explanation of the theoretical underpinnings of mZI models.
  • Development and introduction of the mzim R package for mZIP and mZINB analyses.
  • Application of the mzim package to a dataset on youth abuse experiences, comparing mZINB with traditional models.

Main Results:

  • Marginalized zero-inflated (mZI) models directly estimate population-averaged effects, simplifying interpretation compared to traditional models.
  • The mzim R package provides accessible implementation of mZIP and mZINB models.
  • Analysis using mZINB revealed practical benefits for understanding the overall effect of predictors in zero-inflated data.

Conclusions:

  • Marginalized zero-inflated models offer a more direct and interpretable approach to analyzing count data with excess zeros.
  • The mzim R package significantly enhances the accessibility of mZI models for applied researchers.
  • mZI models, facilitated by the mzim package, provide valuable insights for psychological and health sciences research.