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2016 | OriginalPaper | Chapter

3. Adaptive Regression Modeling of Univariate Continuous Outcomes in SAS

Authors : George J. Knafl, Kai Ding

Published in: Adaptive Regression for Modeling Nonlinear Relationships

Publisher: Springer International Publishing

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Abstract

This chapter describes how to use the genreg (for general regression) macro for adaptive regression modeling, with models for the means linear in their intercept and slope parameters, and its generated output in the special case of univariate continuous outcomes as also covered in Chap. 2. Example code and output are provided addressing analyses of death rates per 100,000 for 60 metropolitan statistical areas in terms of the nitric oxide pollution index, the sulfur dioxide pollution index, and the average annual precipitation. Issues covered include loading the data; setting the number k of folds for computing k-fold likelihood cross-validation (LCV) scores; generating standard polynomial models, fractional polynomial models, monotonic models, and zero-intercept models; incorporating log transforms and multiple primary predictors; model selection using penalized likelihood criteria (PLCs) rather than LCV; bounding primary predictors; residual analyses; and modeling variances as well as means. Practice exercises are also provided for conducting analyses similar to those presented in Chaps. 2 and 3.

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Literature
go back to reference Carpenter, A. (2004). Carpenter’s complete guide to the SAS macro language (2nd ed.). Cary, NC: SAS Institute. Carpenter, A. (2004). Carpenter’s complete guide to the SAS macro language (2nd ed.). Cary, NC: SAS Institute.
go back to reference Freund, R., & Littell, R. (2000). SAS system for regression (3rd ed.). Cary, NC: SAS Institute. Freund, R., & Littell, R. (2000). SAS system for regression (3rd ed.). Cary, NC: SAS Institute.
go back to reference Littell, R. C., Stroup, W. W., & Freund, R. J. (2002). SAS for linear models (4th ed.). Cary, NC: SAS Institute. Littell, R. C., Stroup, W. W., & Freund, R. J. (2002). SAS for linear models (4th ed.). Cary, NC: SAS Institute.
go back to reference Royston, P., & Altman, D. G. (1994). Regression using fractional polynomials of continuous covariates: Parsimonious parametric modeling. Applied Statistics, 43, 429–467.CrossRef Royston, P., & Altman, D. G. (1994). Regression using fractional polynomials of continuous covariates: Parsimonious parametric modeling. Applied Statistics, 43, 429–467.CrossRef
Metadata
Title
Adaptive Regression Modeling of Univariate Continuous Outcomes in SAS
Authors
George J. Knafl
Kai Ding
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-33946-7_3

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