Stepwise and Best Subset Logit Regression
Creates best-subset and stepwise binary logit (logistic) regression for continuous and categorical predictors (use also Generalized Linear Models for ANOVA/ANCOVA-like designs); builds a generalized linear model for categorical and continuous predictor variables, for one or more categorical dependent variables (one at a time).
General
Element Name | Description |
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Model building method | Specifies a model building method. |
Detail of computed results reported | Specifies the level of computed results reported. If Minimal results is requested, only the parameter estimates for the final model will be reported. If All results is requested, STATISTICA will also report summary fit indices, descriptive statistics, etc. |
Intercept | Specifies whether the intercept (constant) is to be included in the model. |
Construct factorial to degree | Specifies the factorial degree of the design to be tested; Statistica will construct a factorial design for all categorical predictors up to the specified degree (i.e., by default up to degree 1, so that the final model will include only main effects for categorical predictors; if you set this parameter to 2, all two-way interactions will also be included, and so on). |
Sweep delta 1.E- | Specifies the negative exponent for a base-10 constant Delta (delta = 10^-sdelta); the default value is 7. Delta is used in sweeping, to detect redundant columns in the design matrix. |
Convergence 1.E- | Specifies the negative exponent for a base-10 constant Delta (delta = 10^-idelta); the default value is 12. Delta is used to check for convergence. |
Maximum number of iterations | Specifies maximum number of iterations for estimating the parameters of the model. |
Generates data source, if N for input less than | Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations. |
Parameters for Best-Subset Selection
Element Name | Description |
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Best subsets measure | Specifies the selection criterion for best subset selection of predictors. |
Number of subsets to display | Specifies the number of subsets to display in the results; STATISTICA will keep a log of the best k predictor models of any given size, using k as specified by this parameter. |
Deployment
Deployment is available if the Statistica installation is licensed for this feature.
Element Name | Description |
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Generates C/C++ code | Generates C/C++ code for deployment of predictive model. |
Generates SVB code | Generates Statistica Visual Basic code for deployment of predictive model. |
Generates PMML code | Generates PMML (Predictive Models Markup Language) code for deployment of predictive model. This code can be used via the Rapid Deployment options to efficiently compute predictions for (score) large data sets. |
Saves C/C++ code | Save C/C++ code for deployment of predictive model. |
File name for C/C code | Specify the name and location of the file where to save the (C/C++) deployment code information. |
Saves SVB code | Save Statistica Visual Basic code for deployment of predictive model. |
File name for SVB code | Specify the name and location of the file where to save the (SVB/VB) deployment code information. |
Saves PMML code | Saves PMML (Predictive Models Markup Language) code for deployment of predictive model. This code can be used via the Rapid Deployment options to efficiently compute predictions for (score) large data sets. |
File name for PMML (XML) code | Specify the name and location of the file where to save the (PMML/XML) deployment code information. |
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