By George J. Knafl,Kai Ding
This e-book provides equipment for investigating even if relationships are linear or nonlinear and for adaptively becoming applicable types after they are nonlinear. information analysts will find out how to include nonlinearity in a single or extra predictor variables into regression types for various sorts of consequence variables. Such nonlinear dependence is usually now not thought of in utilized learn, but nonlinear relationships are universal and so have to be addressed. a customary linear research can produce deceptive conclusions, whereas a nonlinear research delivers novel insights into facts, no longer another way possible.
A number of examples of some great benefits of modeling nonlinear relationships are provided in the course of the ebook. tools are coated utilizing what are known as fractional polynomials according to real-valued energy modifications of fundamental predictor variables mixed with version choice in keeping with probability cross-validation. The ebook covers how you can formulate and behavior such adaptive fractional polynomial modeling within the commonplace, logistic, and Poisson regression contexts with non-stop, discrete, and counts results, respectively, both univariate or multivariate. The ebook additionally offers a comparability of adaptive modeling to generalized additive modeling (GAM) and a number of adaptive regression splines (MARS) for univariate results.
The authors have created personalized SAS macros to be used in carrying out adaptive regression modeling. those macros and code for engaging in the analyses mentioned within the ebook can be found in the course of the first author's site and on-line through the book’s Springer web site. designated descriptions of ways to take advantage of those macros and interpret their output seem in the course of the publication. those equipment may be carried out utilizing different programs.
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health) by George J. Knafl,Kai Ding