Adaptive Regression for Modeling Nonlinear Relationships by George J. Knafl, Kai Ding
By George J. Knafl, Kai Ding
This ebook offers equipment for investigating even if relationships are linear or nonlinear and for adaptively becoming acceptable types once they are nonlinear. info analysts will the way to comprise nonlinearity in a single or extra predictor variables into regression types for various different types of end result variables. Such nonlinear dependence is usually now not thought of in utilized study, but nonlinear relationships are universal and so have to be addressed. a typical linear research can produce deceptive conclusions, whereas a nonlinear research provides novel insights into info, no longer differently attainable.
A number of examples of some great benefits of modeling nonlinear relationships are offered during the booklet. tools are lined utilizing what are known as fractional polynomials according to real-valued strength adjustments of basic predictor variables mixed with version choice according to probability cross-validation. The ebook covers the way to formulate and behavior such adaptive fractional polynomial modeling within the typical, 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 engaging in adaptive regression modeling. those macros and code for engaging in the analyses mentioned within the publication can be found during the first author's web site and on-line through the book’s Springer web site. unique descriptions of the way to take advantage of those macros and interpret their output look through the booklet. those equipment should be carried out utilizing different courses.
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Extra info for Adaptive Regression for Modeling Nonlinear Relationships
Ekwall & M. ), Micro electro mechanical systems (MEMS) technology, fabrication processes and applications, Chapter 1 (pp. 1–66). Hauppauge, NY: Nova. Retrieved from https://www. products_id=19133 Knafl, G. , Fennie, K. , & Williams, A. B. (2004). Electronic monitoring device event modeling on an individual-subject basis using adaptive Poisson regression. Statistics in Medicine, 23, 783–801. Knafl, G. , Fennie, K. , & O’Malley, J. P. (2006). Adaptive repeated measures modeling using likelihood cross-validation.
36 %. These results suggest that recommended sets of power transforms will often be competitive alternatives to adaptive models but can also be more complex. For the simulated data of Sect. 041544 among all recommended degree 2 models. 2 %. 19 % for the data. These results indicate that, while recommended degree 2 models can provide distinct improvements over degree 1 recommended models, they can still have substantially lower LCV scores than adaptively generated models when true powers are outside the range of recommended powers.
Knafl, G. , & Riegel, B. (2014). What puts heart failure patients at risk for poor medication adherence? Patient Preference and Adherence, 8, 1007–1018. , & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13–22. , & Nelder, J. A. (1999). ). Boca Raton, FL: Chapman & Hall/CRC. , & Knafl, G. J. (2014). Electronically monitored medication adherence predicts hospitalization in heart failure patients. Patient Preference and Adherence, 8, 1–13. , & Altman, D.