GRM Analysis Wizard Extended Options - Advanced Tab

Select the Advanced tab of the GRM Analysis Wizard Extended Options dialog to access the options described here. See Model Building for further information on the options of this tab.

Element Name Description
All effects Select the All effects option button to enter all effects specified in the current design (see Between Effects) into the regression equation.
Forward stepwise, Backward stepwise, Forward entry, Backward removal Select these option buttons to perform stepwise selection of predictor variables and effects. When any of these option buttons are selected, various additional options will become available; see the Stepwise Options for details. Forward selection will cause variables to be moved into the model, backward selection will start with a model with all predictor variables and effects in the model, which are then removed. The Forward entry and Backward removal options will only allow for variables or effects to be entered or to be removed, respectively, depending on the chosen method (forward or backward). The Forward stepwise and Backward stepwise options will at each step cause the program to consider simultaneously the addition or removal of a variable or effect, based on the current specifications of p1, enter or F1, enter values. See Stepwise Options for additional details.

For example, if Forward stepwise is selected, STATISTICA will at each step consider both a step "forward", i.e., entry of another variable or effect into the model (based on the p1, enter or F1, enter), and a step "backward", i.e., removal of a previously entered variable or effect from the model (based on the p2, remove or F2, remove). The reason the Forward stepwise method usually adds rather than removes variables or effects (i.e., the reason why it is a forward selection method) is because of the required setting of the p1, enter or F1, enter and p2, remove or F2, remove values, which have to be specified so that p1, enter is smaller (F1, enter is larger) than the p2, remove (F2, remove), thus guaranteeing that significant predictor variables or effects are entered into the model, and not removed. Most of the widely used algorithms for stepwise selection of predictor variables in multiple regression use the Forward stepwise and Backward stepwise methods.

Note: you can force selected effects into the model (i.e., they will always be entered, and never be removed); specifically, when the Effects to force value (see below) k is greater than 0 (zero), then the first k effects in the design (see Between Effects) will be forced into all models that are evaluated.
Best subsets Select the Best subsets option button to perform a search of all possible subsets of effects specified in the current design (see Between Effects). When this option button is set, various additional options will become available for steering the search for the best subset; see Best Subset Options for details. As discussed in the Introductory Overview, the total number of all possible subsets (that need to be reviewed by STATISTICA) can become excessively large, when there are many effects in the model, and many large subset sizes are being considered (via the Start and Stop options). Thus, carefully review the Best Subset Options before clicking OK on the GRM Quick Specs dialog. Note that you can force selected effects into the model (i.e., they will be part of every subset of predictors or effects that is considered); specifically, when the Effects to force value (see below) k is greater than 0 (zero), then the first k effects in the design will be forced into all models that are evaluated. Refer to the Introductory Overview for a discussion of best subset regression.
Note: you can run both Stepwise and Best subsets with multiple dependent variables. In that case, the F/p, enter/remove are taken from the respective F/p values for the multivariate Wilks' Lambda test; for Best subsets regression, none of the univariate criteria are available (Mallows, R-square, adjusted R-square),and instead the selection of the best subset is based on the p for the multivariate test (Wilks' Lambda).
Cross-validation Click the Cross-validation button to display the Cross-Validation dialog for specifying a categorical variable and a (code) value to identify observations that should be included in the computations for fitting the model (the analysis sample); all other observations with valid data for all predictor variables and dependent variables will automatically be classified as belonging to the validation sample (see the Residuals tab for a description of the available residual statistics for observations in the validation sample); note that all observations with valid data for all predictor variables but missing data for the dependent variables will automatically be classified as belonging to the prediction sample (see the Residuals tab for a description of available statistics for the prediction sample).
Effects to force The Effects to force value allows you to force selected effects into the model (i.e., they will be part of every model that is considered); specifically, when the Effects to force value k is greater than 0 (zero), then the first k effects in the design (see Between Effects) will be forced into all models that are evaluated. Refer to Model Building in GRM in the Introductory Overview for a discussion of best subset regression. This option is only available if either one of the stepwise model building methods (Forward stepwise, Backward stepwise) or Best subset regression is selected.
No intercept Select the No intercept check box to exclude the intercept from the model. This option is not available, and no-intercept is the default, for mixture models (see also Experimental Design for a discussion of designs for mixtures).
Lack of fit Select the Lack of fit check box to compute the sums of squares for the pure error, i.e., the sums of squares within all unique combinations of values for the (continuous and categorical) predictor variables. On the Results dialog, options are available to test the lack-of-fit hypothesis. Note that in large designs with continuous predictors, the computations necessary to estimate the pure error can be very time consuming. See Lack-of-Fit Tests Using Pure Error for a discussion of lack-of-fit tests and pure error; see also Experimental Design.
Sweep delta Enter the negative exponent for a base-10 constant delta (delta = 10-sdelta) in the Sweep delta field; the default value is 7. Delta is used (1) in sweeping, to detect redundant columns in the design matrix, and (2) for evaluating the estimability of hypotheses; specifically a value of 2*delta is used for the estimability check.
Inverse delta Enter the negative exponent for a base-10 constant delta (delta = 10-idelta) in the Inverse delta field; the default value is 12. Delta for matrix inversion is used to check for matrix singularity in matrix inversion calculations.