JMP 11 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
Capabilities Index
Fitting Linear Models
• Generalized Regression Models
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Generalized Regression Models
Build Models Using Regularization Techniques
In JMP Pro, the Fit Model platform’s Generalized Regression personality provides 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 evidence multicollinearity issues. Modern data sets often 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 obtain better 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 a zero-inflated count. 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.
The Solution Path for an Elastic Net Fit
Contents
Generalized Regression Overview
Example of Generalized Regression
Launch the Generalized Regression Personality
Model Fit Reports
Model Summary
Solution Path
Parameter Estimates for Centered and Scaled Predictors
Parameter Estimates for Original Predictors
Effect Tests
Model Fit Options
Generalized Regression Options
Additional Examples of the Generalized Regression Personality
Poisson Generalized Regression
Binomial Generalized Regression
Zero-Inflated Poisson Regression
Statistical Details