By George J. Knafl,Kai Ding
This ebook provides equipment for investigating even if relationships are linear or nonlinear and for adaptively becoming acceptable versions once they are nonlinear. facts analysts will find out how to contain nonlinearity in a single or extra predictor variables into regression versions for various forms of final result variables. Such nonlinear dependence is usually now not thought of in utilized examine, but nonlinear relationships are universal and so have to be addressed. a customary linear research can produce deceptive conclusions, whereas a nonlinear research grants novel insights into information, now not differently possible.
A number of examples of the advantages of modeling nonlinear relationships are provided through the booklet. equipment are coated utilizing what are known as fractional polynomials in accordance with real-valued energy changes of fundamental predictor variables mixed with version choice in line with probability cross-validation. The booklet covers the way to formulate and behavior such adaptive fractional polynomial modeling within the regular, 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 engaging in the analyses mentioned within the e-book can be found during the first author's site and on-line through the book’s Springer site. certain descriptions of ways to exploit those macros and interpret their output look through the booklet. those equipment might be applied utilizing different programs.
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