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glmmPen: High Dimensional Penalized Generalized Linear Mixed Models.

Hillary M Heiling1, Naim U Rashid1, Quefeng Li1

  • 1University of North Carolina Chapel Hill.

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|May 31, 2024
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Summary
This summary is machine-generated.

The glmmPen R package enables simultaneous selection of fixed and random effects in high-dimensional generalized linear mixed models (GLMMs). This approach overcomes limitations of traditional methods, improving model accuracy for complex datasets.

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Area of Science:

  • Statistics
  • Computational Biology
  • Biostatistics

Background:

  • Generalized linear mixed models (GLMMs) are essential for analyzing correlated, non-Gaussian data.
  • Accurate selection of fixed and random effects is crucial to prevent bias in GLMMs.
  • Previous methods for joint effect selection were limited to lower-dimensional problems.

Purpose of the Study:

  • Introduce the R package glmmPen for high-dimensional GLMMs.
  • Develop a penalized modeling framework for joint fixed and random effects selection.
  • Provide an efficient computational algorithm for parameter estimation.

Main Methods:

  • Utilize a penalized generalized linear mixed model framework.
  • Employ a Monte Carlo expectation conditional minimization (MCECM) algorithm for parameter estimation.
  • Leverage Stan and RcppArmadillo for computational efficiency in the glmmPen package.

Main Results:

  • The glmmPen package facilitates joint selection of fixed and random effects in high-dimensional GLMMs.
  • The MCECM algorithm provides efficient parameter estimation.
  • Simulations demonstrate good performance in selecting both fixed and random effects.

Conclusions:

  • glmmPen offers a novel solution for high-dimensional GLMMs, addressing limitations in effect selection.
  • The package supports Binomial, Gaussian, and Poisson families with various penalty functions.
  • This method improves the accuracy and reliability of GLMM analysis in complex research areas.