JMP 12 Online Documentation (English)
Discovering JMP
Using JMP
Basic Analysis
Essential Graphing
Profilers
Design of Experiments Guide
Fitting Linear Models
Specialized Models
Multivariate Methods
Quality and Process Methods
Reliability and Survival Methods
Consumer Research
Scripting Guide
JSL Syntax Reference
JMP iPad Help
JMP Interactive HTML
Capabilities Index
JMP 13.2 Online Documentation
Fitting Linear Models
• Mixed Models
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Mixed Models
Jointly Model the Mean and Covariance
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.
An option is available to show a Variogram report, which can help you assess if a spatial covariance model is needed.
Surface Profiler for a Repeated Measures Experiment
Contents
Overview of the Mixed Model Personality
Example Using Mixed Model
Launch the Mixed Model Personality
The Fit Mixed Report
Model Reports
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
Marginal Model Inference
Conditional Model Inference
Save Columns
Additional Examples
Statistical Details
Convergence Score Test
Random Coefficient Model
Repeated Measures
Spatial and Temporal Variability
The Kackar-Harville Correction