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Fitting Linear Models > Generalized Linear Mixed Models
Publication date: 05/05/2023

Image shown hereGeneralized Linear Mixed Models

Fit a Variety of Mixed Models to Non-Normal Response Data

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

The Generalized Linear Mixed Model personality fits models that have a non-Gaussian response variable and random design effects such as blocking. Using the generalized linear model framework enables you to accurately estimate standard errors for the parameters. Using the mixed model framework enables you to accurately represent random effects. The GLMM framework combines these two approaches and gives you the power to test hypotheses and have accurate estimation. The Generalized Linear Mixed Model personality can fit models for count and binomial response variables.

Figure 9.1 Conditional Model Profiler for a Generalized Linear Mixed ModelĀ 

Conditional Model Profiler for a Generalized Linear Mixed Model

Contents

Overview of the Generalized Linear Mixed Models Personality

Example of a Generalized Linear Mixed Model

Launch the Generalized Linear Mixed Model Personality

Fit Model Launch Window
Data Format

Generalized Linear Mixed Model Options

Model Fit Reports

Fit Statistics and Model Summary
Random Effects Covariance Parameter Estimates
Fixed Effects Parameter Estimates
Random Coefficients
Fixed Effects Tests

Model Fit Options

Additional Example of the Generalized Linear Mixed Model Personality

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