By George J. Knafl,Kai Ding
This publication provides equipment for investigating even if relationships are linear or nonlinear and for adaptively becoming applicable types after they are nonlinear. information analysts will the right way to comprise nonlinearity in a single or extra predictor variables into regression versions for various different types of consequence variables. Such nonlinear dependence is usually now not 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 promises novel insights into info, now not another way possible.
A number of examples of the advantages of modeling nonlinear relationships are offered through the booklet. tools are lined utilizing what are referred to as fractional polynomials in keeping with real-valued strength alterations of basic predictor variables mixed with version choice in line with probability cross-validation. The ebook covers the right 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 booklet 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 carrying out adaptive regression modeling. those macros and code for accomplishing the analyses mentioned within the publication can be found in the course of the first author's web site and on-line through the book’s Springer web site. certain descriptions of the way to take advantage of those macros and interpret their output look through the booklet. those equipment will 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