By George J. Knafl,Kai Ding
This ebook provides tools for investigating no matter if relationships are linear or nonlinear and for adaptively becoming acceptable types after they are nonlinear. facts analysts will easy methods to comprise nonlinearity in a single or extra predictor variables into regression versions for various varieties of final result 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 supplies novel insights into facts, no longer another way possible.
A number of examples of the advantages of modeling nonlinear relationships are awarded through the ebook. tools are coated utilizing what are known as fractional polynomials according to real-valued strength variations of basic predictor variables mixed with version choice in line with chance cross-validation. The publication covers how one can formulate and behavior such adaptive fractional polynomial modeling within the ordinary, 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 custom-made SAS macros to be used in accomplishing adaptive regression modeling. those macros and code for engaging in the analyses mentioned within the e-book can be found in the course of the first author's 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 seem through 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