|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|
|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|
|Fixed- and random-effects design matrices|
|Extract covariance parameters of linear mixed-effects model|
|Compute partial dependence （自 R2020b 起）|
|Residuals of fitted linear mixed-effects model|
|Response vector of the 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.