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Polyelectrolyte Complex for Heparin Binding Domain Osteogenic Growth Factor Delivery
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Estimating Complex Measurement and Growth Models Using the R Package PLmixed.

Nicholas J Rockwood1, Minjeong Jeon2

  • 1a The Ohio State University.

Multivariate Behavioral Research
|April 16, 2019
PubMed
Summary

This study introduces PLmixed, an R package for estimating complex measurement and growth models. It uses profile maximum likelihood estimation, offering a unified framework for statistical analysis in behavioral sciences.

Keywords:
GLMMIRTcrossed random effectsgrowth modelslme4partially linear mixed models

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Behavioral Science

Background:

  • Measurement error significantly impacts educational, psychological, and behavioral research.
  • Factor analysis and item response theory models are common but can be complex to implement.
  • Generalized linear mixed models offer a unifying framework for various multilevel and longitudinal models.

Purpose of the Study:

  • Introduce the R package PLmixed for advanced statistical modeling.
  • Provide a tool for estimating complex measurement and growth models.
  • Facilitate the application of profile maximum likelihood estimation.

Main Methods:

  • Utilizing the R package lme4 and the optim function.
  • Implementing profile maximum likelihood estimation for model fitting.
  • Developing the PLmixed package for complex statistical analyses.

Main Results:

  • Demonstrated the utility of PLmixed through two practical examples.
  • Showcased the package's ability to handle complex measurement and growth models.
  • Provided a foundation for further statistical modeling advancements.

Conclusions:

  • PLmixed offers a robust solution for estimating complex statistical models.
  • The package integrates seamlessly with existing R functionalities.
  • It supports a broad range of multilevel and longitudinal analyses.