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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Testing polynomial covariate effects in linear and generalized linear mixed models.

Mingyan Huang1, Daowen Zhang

  • 1Department of Statistics, North Carolina State University, Raleigh, NC 27695.

Statistics Surveys
|October 10, 2009
PubMed
Summary
This summary is machine-generated.

This study reviews methods for testing linear covariate effects in mixed models. It compares polynomial covariate effects using R tests, likelihood ratio tests, score tests, and residual-based tests.

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Area of Science:

  • Statistics
  • Statistical Modeling

Background:

  • Linear mixed models (LMMs) and generalized linear mixed models (GLMMs) are widely used.
  • A key assumption is the linear relationship between transformed response and covariate effects.

Purpose of the Study:

  • To review and compare methods for testing polynomial covariate effects in LMMs and GLMMs.
  • To assess the adequacy of the linear covariate effects assumption.

Main Methods:

  • Review of four hypothesis testing approaches: R tests, likelihood ratio tests, score tests, and residual-based tests.
  • Discussion of the derivation and performance of each testing procedure.
  • A simulation study comparing likelihood ratio tests and score tests.

Main Results:

  • The paper provides a comprehensive overview of hypothesis testing procedures for polynomial covariate effects.
  • The simulation study offers insights into the comparative performance of likelihood ratio and score tests.

Conclusions:

  • Testing the linearity assumption of covariate effects is crucial for LMMs and GLMMs.
  • The reviewed methods offer various approaches to assess model adequacy.