A linear mixed-effects 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. You can fit a linear mixed-effects model using
fitlme if your data is in a
table. Alternatively, if your model is not easily described using a formula,
you can create matrices to define the fixed and random effects, and then fit
the model using
Estimate Responses and Effects
|Predict response of linear mixed-effects model|
|Generate random responses from fitted linear mixed-effects model|
|Estimates of fixed effects and related statistics|
|Estimates of random effects and related statistics|
|Fitted responses from a linear mixed-effects model|
Evaluate ects Model
|Analysis of variance for linear mixed-effects model|
|Confidence intervals for coefficients of linear mixed-effects model|
|Hypothesis test on fixed and random effects of linear mixed-effects model|
|Compare linear mixed-effects models|
|Extract covariance parameters of linear mixed-effects model|
|Compute partial dependence|
|Residuals of fitted linear mixed-effects model|
Visualize ects Model and Summary Statistics
|Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots|
|Plot residuals of linear mixed-effects model|
|Linear mixed-effects model|
- Prepare Data for Linear Mixed-Effects Models
Store data in the correct form for fitting a linear mixed-effects model.
- Relationship Between Formula and Design Matrices
Understand the relationship between a model formula and the design matrices in linear mixed-effects models.
- Linear Mixed-Effects Model Workflow
This example shows how to fit and analyze a linear mixed-effects model (LME).
- Fit Mixed-Effects Spline Regression
This example shows how to fit a mixed-effects linear spline model.
- 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.