Model Fit OptionsShows or hides the Model Summary report that includes information about the specification and goodness of fit statistics for the model. This option also displays the Estimation Details report for applicable models. See Model Summary and Estimation Details.
(Not available for Maximum Likelihood models.) Shows or hides the Solution Path and Validation Path plots. See Solution Path.
Shows or hides a table of centered and scaled parameter estimates. See Parameter Estimates for Centered and Scaled Predictors.
Shows or hides a table of parameter estimates in the original scale of the data. See Parameter Estimates for Original Predictors.
Shows or hides tests for each effect. The effect test for a given effect tests the null hypothesis that all parameters associated with that effect are zero. A nominal or ordinal effect can have several associated parameters, based on its number of levels. The effect test for such an effect tests whether all of the associated parameters are zero. When the Distribution is Multinomial, the effects are combined over the levels of the response. See Effect Tests.
Displays the Prediction Expression report that contains the equation for the estimated model. See Show Prediction Expression in Standard Least Squares Report and Options for an example.
(Not available for responses that have the Vector modeling type.) Opens a Fit Model launch window where the Construct Model Effects list contains only the terms that have nonzero parameter estimates (active effects). All other specifications are those used in the original analysis.
(Available only when the specified Distribution is Binomial and the model contains an intercept. Not available for responses that have the Vector modeling type.) Displays a report that contains odds ratios for categorical predictors, and unit odds ratios and range odds ratios for continuous predictors. An odds ratio is the ratio of the odds for two events. The odds of an event is the probability that the event of interest occurs versus the probability that it does not occur. The event of interest is defined by the Target Level in the Fit Model launch window.
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Unit Odds Ratios Report. The unit odds ratio is calculated over a one-unit change in a continuous model term.
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Range Odds Ratios Report. The range odds ratio is calculated over the entire range of a continuous model term.
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Note: If there are interactions in the model, you can use the Multiple Comparisons option to obtain odds ratios. See Multiple Comparisons.
(Available only when the specified Distribution is Poisson or Negative Binomial and the model contains an intercept.) Displays a report that contains incidence rate ratios for categorical predictors, and unit incidence rate ratios and range incidence rate ratios for continuous predictors. An incidence rate ratio is the ratio of the incidence rate for two events. The incidence rate for a model term is the number of new events that occur over a given time period.
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Unit Incidence Rate Ratios Report. The unit incidence rate ratio is calculated over a one-unit change in a continuous model term.
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Range Incidence Rate Ratios Report. The range incidence rate ratio is calculated over the entire range of a continuous model term.
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(Available only when the specified Distribution is Cox Proportional Hazards and the model contains an intercept.) Displays a report that contains hazard ratios for categorical predictors, and unit hazard ratios and range hazard ratios for continuous predictors. A hazard ratio is the ratio of the hazard rate for two events. The hazard rate at time t for an event is the conditional probability that the event will not survive an additional amount of time, given that it has survived to time t.
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Unit Hazard Ratios Report. The unit hazard ratio is calculated over a one-unit change in a continuous model term.
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Range Hazard Ratios Report. The range hazard ratio is calculated over the entire range of a continuous model term.
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Displays a matrix showing the covariances of the parameter estimates. These are calculated using M-estimation and a sandwich formula (Zou 2006 and Huber and Ronchetti 2009).
Displays a matrix showing the correlations of the parameter estimates. These are calculated using M-estimation and a sandwich formula (Zou 2006 and Huber and Ronchetti 2009).
(Not available for responses that have the Vector modeling type.) Predicts an X value, given specific values for Y and the other X variables. This can be used to predict continuous variables only. For more information about Inverse Prediction, see Inverse Prediction in Standard Least Squares Report and Options.
(Not available for responses that have the Vector modeling type or for models that do not contain any categorical predictors.) Displays the Multiple Comparisons launch window. For more information about the Multiple Comparisons launch window and report, see Multiple Comparisons in Standard Least Squares Report and Options. Note that the multiple comparisons are performed on the linear predictor scale. When the specified Distribution is Binomial, the multiple comparisons are performed on the odds ratios. When the specified Distribution is Poisson, the multiple comparisons are performed on the incidence rate ratios. When the specified Distribution is Cox Proportional Hazards, the multiple comparisons are performed on the hazard ratios.
Displays the Prediction Profiler. Predictors that have parameter estimates of zero and that are not involved in any interaction terms with nonzero coefficients do not appear in the profiler. For details about the prediction profiler, see Profiler in the Profilers book.
Displays a Custom Test report that enables you to test a custom hypothesis. If the model has a Solution Path, the custom test results update as you update the solution. For more information about custom tests, see Custom Test in Standard Least Squares Report and Options. The Custom Test red triangle menu contains an option to remove the Custom Test report.
Provides various plots to help assess how well the current model fits. If a Validation column is specified or if KFold, Holdback, or Leave-One-Out is selected as the Validation Method, the options below enable you to view the training, validation, and, if applicable, test sets, or they construct separate plots for these sets. If KFold or Leave-One-Out is selected, then the plots correspond to the validation set that optimizes prediction error, and its corresponding training set. See KFold.
The ROC curve measures the ability of the fitted probabilities to classify response levels correctly. The further the curve from the diagonal, the better the fit. An introduction to ROC curves is found in ROC Curves in the Basic Analysis book.
If the response has more than two levels, the ROC Curve plot displays an ROC curve for each response level. For a given response level, this curve is the ROC curve for correct classification into that level. See ROC Curve in the Predictive and Specialized Modeling book for more information about ROC curves.
If the response has more than two levels, the Lift Curve plot displays a lift curve for each response level. For a given response level, this curve is the lift curve for correct classification into that level. See Lift Curve in the Predictive and Specialized Modeling book for more information about lift curves.
Enables you to save columns based on the fitted model to the data table. See Save Columns Options for Cox Proportional Hazards Models for the options that are available if Cox Proportional Hazards is selected as the Distribution. For all other Distributions, the following columns can be saved to the data table:
Saves a column to the data table that contains the prediction formula, given in terms of the observed (unstandardized) data values. The prediction formula does not contain zeroed terms. See Statistical Details for Distributions for mean formulas.
Note: You can change the α level for the confidence interval in the Fit Model window by selecting Set Alpha Level from the red triangle menu.
Saves a column to the data table that contains a formula for the variance of the prediction. The variance of the prediction is calculated using the formula for the variance of the selected Distribution. The value of the parameter involved in the link function is estimated by applying the inverse of the link function to the estimated linear component. Other parameters are replaced by their estimates. See Statistical Details for Distributions for variance formulas. Not available if Binomial is selected as the Distribution.
Saves a column to the data table that contains a formula for the product of the design matrix and the vector of parameter estimates. This is commonly referred to as Xβ. The formula does not contain zeroed terms.
Saves a column to the data table that contains a formula that generates simulated values using the estimated parameters for the model that you fit. This column can be used in the Simulate utility as a Column to Switch In. See Simulate in Basic Analysis.
(Available only if the specified Distribution is Normal and the specified Estimation Method is Standard Least Squares.) Saves a column to the data table that contains the values for Cook’s D Influence statistic.
(Available only if the specified Distribution is Normal and the specified Estimation Method is Standard Least Squares.) Saves a column to the data table that contains the diagonal elements of X(X‘X)-1X‘. These values are sometimes called hat values.
Creates prediction formulas and saves them as formula column scripts in the Formula Depot platform. If a Formula Depot report is not open, this option creates a Formula Depot report. See Formula Depot in the Predictive and Specialized Modeling book.
Saves a column to the data table that contains a formula for the Cox-Snell residuals. The Cox-Snell residuals are strictly positive. See Meeker and Escobar (1998, sec. 17.6.1) for a discussion of Cox-Snell residuals.
Saves a column to the data table that contains a formula for the martingale residuals. The martingale residual is defined as the difference between the observed number of events for an individual and a conditionally expected number of events. The martingale residuals have a mean of zero and range between negative infinity and 1. See Fleming and Harrington (1991).
Saves a column to the data table that contains a formula for the product of the design matrix and the vector of parameter estimates. This is commonly referred to as Xβ. The formula does not contain zeroed terms.