By George J. Knafl,Kai Ding
This e-book provides tools for investigating even if relationships are linear or nonlinear and for adaptively becoming applicable types after they are nonlinear. facts analysts will the right way 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 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 information, no longer in a different way possible.
A number of examples of some great benefits of modeling nonlinear relationships are offered during the e-book. equipment are coated utilizing what are known as fractional polynomials according to real-valued energy adjustments of fundamental predictor variables mixed with version choice in response to chance cross-validation. The publication covers the way to formulate and behavior such adaptive fractional polynomial modeling within the average, 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 personalized SAS macros to be used in carrying out 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 web 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 tools could be applied utilizing different programs.
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