Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Adaptive Regression for Modeling Nonlinear RelationshipsGeneralized Additive Modeling in SAS

Adaptive Regression for Modeling Nonlinear Relationships: Generalized Additive Modeling in SAS [This chapter provides a description of how to use PROC GAM for generating generalized additive models (GAMs) for univariate continuous and dichotomous outcomes as well as how to evaluate and compare GAMs with likelihood cross-validation (LCV) scores. Comparison of GAMS to adaptive fractional polynomial models on the basis of LCV scores is also covered. Example code is provided for generating models for predicting the univariate continuous outcome death rate per 100,000 in terms of available predictors as also addressed in Chaps. 2, 3, 6, 7 and 16 as well as models for predicting the univariate dichotomous outcome a high mercury level in fish over 1.0 ppm versus a lower level in terms of available predictors as also addressed in Chaps. 8, 9 and 16.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Adaptive Regression for Modeling Nonlinear RelationshipsGeneralized Additive Modeling in SAS

Download PDF
12 pages

Loading next page...
 
/lp/springer-journals/adaptive-regression-for-modeling-nonlinear-relationships-generalized-qBvQoAjp97

References (4)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-33944-3
Pages
315 –327
DOI
10.1007/978-3-319-33946-7_17
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter provides a description of how to use PROC GAM for generating generalized additive models (GAMs) for univariate continuous and dichotomous outcomes as well as how to evaluate and compare GAMs with likelihood cross-validation (LCV) scores. Comparison of GAMS to adaptive fractional polynomial models on the basis of LCV scores is also covered. Example code is provided for generating models for predicting the univariate continuous outcome death rate per 100,000 in terms of available predictors as also addressed in Chaps. 2, 3, 6, 7 and 16 as well as models for predicting the univariate dichotomous outcome a high mercury level in fish over 1.0 ppm versus a lower level in terms of available predictors as also addressed in Chaps. 8, 9 and 16.]

Published: Sep 21, 2016

Keywords: Dichotomous Outcome; Thin Plate Spline; Model Deathrate; Practice Exercise; Nonparametric Component

There are no references for this article.