Generalized Additive Models Button

Click the button to display the Generalized Additive Models Startup Panel. Like generalized linear models, generalized additive models can be used to predict a dependent variable from various distributions (e.g., Binomial, Normal, Gamma, Poisson), from one or more continuous or categorical predictor variables, using various link functions (e.g., Logit, Log, Inverse, Identity); in addition, in generalized additive models each predictor variable is smoothed via a cubic spline smoother, to yield the best fit of the model, and best prediction of the dependent variable. The STATISTICA Generalized Additive Models facilities are an implementation of methods developed and popularized by Hastie and Tibshirani (1990). The module will handle continuous and categorical predictor variables and allow you to choose from a wide variety of distributions for the dependent variable and link functions for the effects of the predictor variables on the dependent variable.

Please consult the product description brochure for sales information concerning this module, and specifically, which modules are included in each STATISTICA product.