Fitting Linear Models > Generalized Regression Models
Publication date: 08/13/2020

Generalized Regression Models

Build Models Using Variable Selection Techniques

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

In JMP Pro, the Fit Model platform’s Generalized Regression personality provides variable selection techniques, including shrinkage techniques, that specifically address modeling correlated and high-dimensional data. Two of these techniques, the Lasso and the Elastic Net, perform variable selection as part of the modeling procedure.

Large data sets that contain many variables typically exhibit multicollinearity issues. Modern data sets can include more variables than observations, requiring variable selection if traditional modeling techniques are to be used. The presence of multicollinearity and a profusion of predictors exposes the shortcomings of classical techniques.

Even for small data sets with little or no correlation, including designed experiments, the Lasso and Elastic Net are useful. They can be used to build predictive models or to select variables for model reduction or for future study.

The Generalized Regression personality is useful for many modeling situations. This personality enables you to specify a variety of distributions for your response variable. Use it when your response is continuous, binomial, a count, or zero-inflated. Use it when you are interested in variable selection or when you suspect collinearity in your predictors. More generally, use it to fit models that you compare to models obtained using other techniques.

Figure 6.1 The Solution Path for an Elastic Net Fit 


Overview of the Generalized Regression Personality

Example of Generalized Regression

Launch the Generalized Regression Personality


Generalized Regression Report Window

Generalized Regression Report Options

Model Launch Control Panel

Response Distribution
Estimation Method Options
Advanced Controls
Validation Method Options
Early Stopping

Model Fit Reports

Regression Plot
Model Summary
Estimation Details
Solution Path
Parameter Estimates for Centered and Scaled Predictors
Parameter Estimates for Original Predictors
Active Parameter Estimates
Effect Tests

Model Fit Options

Statistical Details for the Generalized Regression Personality

Statistical Details for Estimation Methods
Statistical Details for Advanced Controls
Statistical Details for Distributions
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