Mixed Effects
A generalized linear mixed-effects (GLME) model includes both fixed and
random effects in modeling a response variable. This type of model can
account for global and local trends in a data set by including the random
effects of a clustering variable. GLME models are a generalization of Linear Mixed-Effects Models (LME) for data where the response
variable is not normally distributed. Create a
GeneralizedLinearMixedModel
object using fitglme
.
Classes
GeneralizedLinearMixedModel | Generalized linear mixed-effects model class |
Functions
Topics
- Fit a Generalized Linear Mixed-Effects Model
This example shows how to fit a generalized linear mixed-effects model (GLME) to sample data.
- 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.