By George J. Knafl,Kai Ding
This e-book offers equipment for investigating even if relationships are linear or nonlinear and for adaptively becoming acceptable versions after they are nonlinear. information analysts will how you can comprise nonlinearity in a single or extra predictor variables into regression versions for various varieties of end result variables. Such nonlinear dependence is usually now not thought of in utilized study, but nonlinear relationships are universal and so must be addressed. a customary linear research can produce deceptive conclusions, whereas a nonlinear research delivers novel insights into info, no longer in a different way possible.
A number of examples of some great benefits of modeling nonlinear relationships are offered in the course of the e-book. tools are lined utilizing what are known as fractional polynomials in line with real-valued energy differences of basic predictor variables mixed with version choice in keeping with probability cross-validation. The ebook covers the right way to formulate and behavior such adaptive fractional polynomial modeling within the common, logistic, and Poisson regression contexts with non-stop, discrete, and counts results, respectively, both univariate or multivariate. The publication additionally presents 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 custom-made SAS macros to be used in undertaking adaptive regression modeling. those macros and code for carrying out the analyses mentioned within the publication can be found throughout the first author's web site and on-line through the book’s Springer web site. particular descriptions of the way to exploit those macros and interpret their output seem during the booklet. those equipment will be applied utilizing different programs.
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health) by George J. Knafl,Kai Ding