An Introduction to Linear Mixed-Effects Modeling in R

Mixed Effects Modeling in R

Earlier in the regression section, we mentioned that the Ultimatum Game dataset we were working with was actually a repeated design, which means that there was a correlated variance structure because all of the observations were nested within each subject. The standard solution to this problem in a GLM framework is to treat subject as a fixed effect and add (n-1) variables indicating a dummy co...

An Introduction to Generalized Linear Mixed Models

Linear mixed models provide a powerful means of predicting breeding values. However, for many traits of economic importance the assumptions of linear responses, constant variance, and normality are questionable. Generalized linear mixed models provide a means of modeling these deviations from the usual linear mixed model. This paper will examine what constitutes a generalized linear mixed model...

An introduction to hierarchical linear modeling

This tutorial aims to introduce Hierarchical Linear Modeling (HLM). A simple explanation of HLM is provided that describes when to use this statistical technique and identifies key factors to consider before conducting this analysis. The first section of the tutorial defines HLM, clarifies its purpose, and states its advantages. The second section explains the mathematical theory, equations, an...

An Introduction to R

R can be viewed as a programming language that happens to come with a large library of pre-defined functions that can be used to perform various tasks. A major focus of these pre-defined functions is statistical data analysis, and these allow R to be used purely as a toolbox for standard statistical techniques. However, some knowledge of R programming is essential to use it well at any level. F...

An Introduction to Modeling Embedded Mixed-Signal Systems