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# Mixed Effects

Linear mixed-effects models

## Classes

 `LinearMixedModel` Linear mixed-effects model class

## Functions

 `fitlme` Fit linear mixed-effects model `fitlmematrix` Fit linear mixed-effects model `disp` Display linear mixed-effects model `predict` Predict response of linear mixed-effects model `random` Generate random responses from fitted linear mixed-effects model `fixedEffects` Estimates of fixed effects and related statistics `randomEffects` Estimates of random effects and related statistics `designMatrix` Fixed- and random-effects design matrices `fitted` Fitted responses from a linear mixed-effects model `response` Response vector of the linear mixed-effects model
 `anova` Analysis of variance for linear mixed-effects model `coefCI` Confidence intervals for coefficients of linear mixed-effects model `coefTest` Hypothesis test on fixed and random effects of linear mixed-effects model `compare` Compare linear mixed-effects models `covarianceParameters` Extract covariance parameters of linear mixed-effects model `partialDependence` Compute partial dependence `plotPartialDependence` Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots `plotResiduals` Plot residuals of linear mixed-effects model `residuals` Residuals of fitted linear mixed-effects model

## Concepts

• Linear Mixed-Effects Models

Linear mixed-effects models are extensions of linear regression models for data that are collected and summarized in groups.

• Estimating Parameters in Linear Mixed-Effects Models

The two most commonly used approaches to parameter estimation in linear mixed-effects models are maximum likelihood and restricted maximum likelihood methods.

• Wilkinson Notation

Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.