Model Building Results in GLZ

Note: results for stepwise or best-subset regression. Unlike in the stepwise or best-subset results in General Regression Models (GRM), the results that can be reviewed from the GLZ Results dialog box always pertain to the full model, regardless of which effects were selected for inclusion during the model building procedure. The reason for this is that, unlike in GRM, the relationship between predictors, and their interactive effects (e.g., two predictors masking the effects of a third) are often much more complex. Also, unlike in GRM, because of the manner in which the p1, enter and p2, remove probabilities are determined (in forward stepwise selection, the score statistic is used to select new (significant) effects; while the Wald statistic is used during backward steps), the Forward stepwise and Backward stepwise methods may result in the repetitive selection and removal of one or more predictors. (See GLZ Quick Specs - Advanced tab for further details on the Stepwise and p options.) Therefore, the stepwise results can be reviewed separately via these options. If, after comparing the overall model (with all effects) with the one suggested by the model building procedure, you decide to further evaluate the latter model, use the Make model button (see below) to transfer that model to the Quick Specs or the GLZ Analysis Syntax Editor dialog, and then fit that model to the data.

Note: stepwise methods. When either forward stepwise, backward stepwise, forward entry, or backward removal are selected on the Quick Specs Dialog - Advanced tab, the spreadsheet will show for each step which effects were in the model at that step, which ones were not in the model, and which one was selected for entry or removal. For each effect in the model, the spreadsheet will show the Wald statistic and respective p-value; for each effect not in the model, the spreadsheet will show the score statistic and respective p-value.

Note: selection of variables for inclusion in or removal from the model. 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 STATISTICA to consider simultaneously the addition or removal of a variable or effect, based on the current specifications of p1, enter or p2, remove. See the p1, enter, p2, remove, and Max steps options on the Quick Specs Dialog - Advanced tab 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 p enter), and a step "backward", i.e., removal of a previously entered variable or effect from the model (based on the p to 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 and p2, remove values, which have to be specified so that p1, enter is smaller than the p2, 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 use the Forward stepwise and Backward stepwise methods.

Note: best subset regression. When the current analysis used the best subset method for selecting effects for the model, the spreadsheet will show the best-fitting subsets that were found, based on the chosen criterion (Likelihood score, Likelihood, Akaike information criterion (AIC) specified on the Quick Specs Dialog - Advanced tab; the respective statistics are also reported in the spreadsheet). Note that the number of best subsets that will be shown in the spreadsheet (i.e., retained from the search computations) can be determined with the Max. subset option on the Quick Specs Dialog - Advanced tab (see the MAXSUB keyword in the GLZ Syntax).

See also, GLZ - Index.