By George J. Knafl,Kai Ding
This ebook offers tools for investigating no matter if relationships are linear or nonlinear and for adaptively becoming acceptable types once they are nonlinear. facts analysts will find out how to include nonlinearity in a single or extra predictor variables into regression types for various varieties of end result variables. Such nonlinear dependence is usually now not thought of in utilized learn, but nonlinear relationships are universal and so must be addressed. a regular linear research can produce deceptive conclusions, whereas a nonlinear research promises novel insights into info, no longer another way possible.
A number of examples of the advantages of modeling nonlinear relationships are offered through the ebook. tools are coated utilizing what are known as fractional polynomials in response to real-valued strength adjustments of basic predictor variables mixed with version choice according to chance cross-validation. The publication covers how one can formulate and behavior such adaptive fractional polynomial modeling within the normal, logistic, and Poisson regression contexts with non-stop, discrete, and counts results, respectively, both univariate or multivariate. The ebook 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 personalized SAS macros to be used in undertaking adaptive regression modeling. those macros and code for carrying out 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 site. unique descriptions of ways to exploit those macros and interpret their output seem in the course of the publication. those equipment will be carried out utilizing different programs.
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