Mixed Effects
Generalized linear mixed-effects models
Classes
GeneralizedLinearMixedModel | Generalized linear mixed-effects model class |
Functions
fitglme | Fit generalized linear mixed-effects model |
disp | Display generalized linear mixed-effects model |
predict | Predict response of generalized linear mixed-effects model |
random | Generate random responses from fitted generalized 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 generalized linear mixed-effects model |
response | Response vector of generalized linear mixed-effects model |
anova | Analysis of variance for generalized linear mixed-effects model |
coefCI | Confidence intervals for coefficients of generalized linear mixed-effects model |
coefTest | Hypothesis test on fixed and random effects of generalized linear mixed-effects model |
compare | Compare generalized linear mixed-effects models |
covarianceParameters | Extract covariance parameters of generalized linear mixed-effects model |
partialDependence | Compute partial dependence |
plotPartialDependence | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |
plotResiduals | Plot residuals of generalized linear mixed-effects model |
residuals | Residuals of fitted generalized linear mixed-effects model |
refit | Refit generalized linear mixed-effects model |
Examples and How To
- Fit a Generalized Linear Mixed-Effects Model
Fit a generalized linear mixed-effects model (GLME) to sample data.
Concepts
- Generalized Linear Mixed-Effects Models
Generalized linear mixed-effects (GLME) models describe the relationship between a response variable and independent variables using coefficients that can vary with respect to one or more grouping variables, for data with a response variable distribution other than normal.
- Wilkinson Notation
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.