By George J. Knafl,Kai Ding
This e-book provides equipment for investigating no matter if relationships are linear or nonlinear and for adaptively becoming applicable versions once they are nonlinear. info analysts will comprise nonlinearity in a single or extra predictor variables into regression types for various different types of end result variables. Such nonlinear dependence is frequently now not thought of in utilized examine, 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, now not differently possible.
A number of examples of the advantages of modeling nonlinear relationships are provided during the ebook. equipment are lined utilizing what are known as fractional polynomials in line with real-valued energy variations of basic predictor variables mixed with version choice in line with chance cross-validation. The ebook 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 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 personalized SAS macros to be used in accomplishing adaptive regression modeling. those macros and code for undertaking the analyses mentioned within the e-book 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 take advantage of those macros and interpret their output seem through the publication. those tools 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