Fitting Linear Models • Mixed Models
Previous
 • 
Next
Mixed 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.
Analytic results are supported by compelling dynamic visualization tools such as profilers, surface plots, and contour plots. These visual displays stimulate, complement, and support your understanding of the model. See the Profilers book for more information.
Figure 7.1 Marginal Model Profiler for a Split Plot Experiment
Contents 
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
Multiple Comparisons
Compare Slopes
Marginal Model Inference
Actual by Predicted Plot
Residual Plots
Profilers
Conditional Model Inference
Actual by Conditional Predicted Plot
Conditional Residual Plots
Conditional Profilers
Variogram
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

Previous
 • 
Next
Help created on 7/12/2018