• Model Summary
 • Estimation Details (shown only for Lasso, Elastic Net, and Ridge)
 • Solution Path (shown for all but the Maximum Likelihood Estimation Method and Quantile Regression)
 • Parameter Estimates for Centered and Scaled Predictors
 • Parameter Estimates for Original Predictors
 • Effect Tests
An extension of the RSquare measure that can be applied to general regression models. Generalized RSquare compares the likelihood of the fitted model (LM) to the likelihood of the intercept-only (constant) model (L0). It is scaled to have a maximum of 1. The Generalized RSquare is defined as follows:
The Parameter Estimates are plotted using the vertical axis of the Solution Path plot. These are the scaled parameter estimates. They are derived for a model expressed in terms of centered and scaled predictors (see Parameter Estimates for Centered and Scaled Predictors).
The horizontal scaling for the Solution Path and Validation plot is given in terms of the Magnitude of Scaled Parameter Estimates. This is the l1 norm, defined as the sum of the absolute values of the scaled parameter estimates for the model for the mean. (Estimates corresponding to the intercept, dispersion parameters, and zero-inflation parameters are excluded from the calculation of the l1 norm.) A vertical red line is placed at the value of the l1 norm for the solution displayed in the Parameter Estimates for Original Predictors report.
The Validation Plot vertical axis varies based on the Validation Method chosen. Validation Plot Vertical Axis by Validation Method illustrates the vertical axis by Validation Method. For all vertical axis options, lower values are better. A vertical red line is placed at the value of the l1 norm tuning parameter for the solution displayed in the Parameter Estimates for Original Predictors report. There is some room for adjustment of the tuning parameter. You can move the red vertical line to the left or right to adjust the tuning parameter. A dashed vertical line remains at the best fit model. For AICc, BIC, KFold, and Leave-One-Out validation methods, the graph indicates regions where the tuning parameter is best kept.
 • The green region indicates there is strong evidence that the model is as good as the best model.
 • The yellow region indicates there is weaker evidence that the model is sufficient.
 Validation Method Vertical Axis Tuning Parameter Regions KFold Scaled -LogLikelihood One Holdback Scaled -LogLikelihood None Leave-One-Out Scaled -LogLikelihood One BIC BIC Two AICc AICc Two Validation Column Scaled -LogLikelihood None
 • The mean is subtracted from each observation.
 • Each difference is then divided by the square root of the sum of the squared differences from the mean.
The p-value for the Wald test.
The p-value for the Wald test.
The p-value for the Wald ChiSquare test.
 • If the effect has one degree of freedom, the word “Removed” appears at the far right in the row for that effect.
 • If the effect has multiple degrees of freedom, the phrase “Levels removed” appears, followed by the number of levels that correspond to terms with parameter estimates of zero.