By George J. Knafl,Kai Ding
This publication provides equipment for investigating no matter if relationships are linear or nonlinear and for adaptively becoming acceptable versions after they are nonlinear. info analysts will find out how to comprise nonlinearity in a single or extra predictor variables into regression versions for various different types of final result variables. Such nonlinear dependence is usually no longer thought of in utilized examine, but nonlinear relationships are universal and so must be addressed. a regular linear research can produce deceptive conclusions, whereas a nonlinear research grants novel insights into facts, no longer differently possible.
A number of examples of the advantages of modeling nonlinear relationships are provided through the publication. equipment are coated utilizing what are referred to as fractional polynomials in accordance with real-valued energy adjustments of basic predictor variables mixed with version choice in keeping with chance cross-validation. The publication covers the best way to 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 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 custom-made SAS macros to be used in engaging in 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. certain descriptions of the way to take advantage of those macros and interpret their output seem through the ebook. those equipment may 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