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Fitting Linear Models > Mixed Models
Publication date: 11/29/2021

Image shown hereMixed Models

Jointly Model the Mean and Covariance

The Mixed Models personality of the Fit Model platform is available only in JMP Pro.

In JMP Pro, the Fit Model platform’s Mixed Model personality fits a wide variety of linear models for continuous responses with complex covariance structures. These models include random coefficients, repeated measures, spatial data, and data with multiple correlated responses. Use the Mixed Model personality to specify linear mixed models and their covariance structures conveniently using an intuitive interface, and to fit these models using maximum likelihood methods.

The modeling results are supported by interactive visualization tools such as profilers, surface plots, and contour plots. You can use these tools to complement your understanding of the model.

Figure 8.1 Marginal Model Profiler for a Split Plot Experiment 

Marginal Model Profiler for a Split Plot Experiment


Overview of the Mixed Model Personality

Example Using Mixed Model

Launch the Mixed Model Personality

Fit Model Launch Window
Data Format

The Fit Mixed Report

Fit Statistics
Random Effects Covariance Parameter Estimates
Fixed Effects Parameter Estimates
Repeated Effects Covariance Parameter Estimates
Random Coefficients
Random Effects Predictions
Fixed Effects Tests
Sequential Tests

Multiple Comparisons

Compare Slopes

Marginal Model Inference

Actual by Predicted Plot
Residual Plots

Conditional Model Inference

Actual by Conditional Predicted Plot
Conditional Residual Plots
Conditional Profilers

Covariance and Correlation Matrices

Save Columns

Additional Examples of the Mixed Models Personality

Repeated Measures Example
Split Plot Example
Spatial Example: Uniformity Trial
Correlated Response Example

Statistical Details for the Mixed Models Personality

Convergence Score Test
Random Coefficient Model
Repeated Measures
Repeated Covariance Structures
Spatial and Temporal Variability
The Kackar-Harville Correction
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