By George J. Knafl,Kai Ding
This publication offers tools for investigating even if relationships are linear or nonlinear and for adaptively becoming applicable types after they are nonlinear. info analysts will the way to contain nonlinearity in a single or extra predictor variables into regression types for various varieties of end result variables. Such nonlinear dependence is frequently no longer thought of in utilized examine, but nonlinear relationships are universal and so have to be addressed. a regular linear research can produce deceptive conclusions, whereas a nonlinear research offers novel insights into info, now not 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 keeping with real-valued strength ameliorations of basic predictor variables mixed with version choice according to probability cross-validation. The e-book covers how you can formulate and behavior such adaptive fractional polynomial modeling within the usual, logistic, and Poisson regression contexts with non-stop, discrete, and counts results, respectively, both univariate or multivariate. The e-book 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 undertaking adaptive regression modeling. those macros and code for engaging in the analyses mentioned within the booklet can be found throughout the first author's web site and on-line through the book’s Springer web site. precise descriptions of the way to take advantage of those macros and interpret their output look during the publication. those equipment could be carried out utilizing different programs.
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