Presenter Information/ Coauthors Information

Jixiang Wu, South Dakota State UniversityFollow

Presentation Type

Event

Track

Tools

Abstract

Linear mixed model (LMM) approaches offer much more flexibility comparing ANOVA (analysis of variance) based methods. There are three commonly used LMM approaches: maximum likelihood, restricted maximum likelihood, and minimum norm quadratic unbiased estimation. These three approaches, however, sometimes could also lead low testing power compared to ANOVA methods. Integration of resampling techniques like jackknife could help improve testing power based on both our simulation studies. In this presentation, I will introduce a R package, minque, which integrates LMM approaches and resampling techniques and demonstrate the use of this packages in various linear mixed model analyses.

Start Date

5-2-2019 10:00 AM

End Date

5-2-2019 10:50 AM

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Feb 5th, 10:00 AM Feb 5th, 10:50 AM

minque: An R Package for Analyzing Various Linear Mixed Models

Pasque 255

Linear mixed model (LMM) approaches offer much more flexibility comparing ANOVA (analysis of variance) based methods. There are three commonly used LMM approaches: maximum likelihood, restricted maximum likelihood, and minimum norm quadratic unbiased estimation. These three approaches, however, sometimes could also lead low testing power compared to ANOVA methods. Integration of resampling techniques like jackknife could help improve testing power based on both our simulation studies. In this presentation, I will introduce a R package, minque, which integrates LMM approaches and resampling techniques and demonstrate the use of this packages in various linear mixed model analyses.