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Adaptive Regression for Modeling Nonlinear Relationships

Part of the Statistics for Biology and Health Book Series

DOI: 10.1007/978-3-319-33946-7

ISBN: 978-3-319-33944-3

Publisher: Springer International Publishing - Springer Journals

Sep 21, 2016

1 - 10 of 20 Chapters

Available

Introduction

Knafl, George J.; Ding, Kai

Pages: 1 –8

[Nonlinearity in predictor (or explanatory or independent) variables in regression models for different types of outcome (or response or dependent) variables is often not considered in applied research. While relationships can reasonably be treated as linear in some cases, it is not unusual for...
Available

Adaptive Regression Modeling of Univariate Continuous Outcomes

Knafl, George J.; Ding, Kai

Pages: 11 –43

[This chapter presents a series of analyses of data on death rates per 100,000 for 60 metropolitan statistical areas, addressing how these death rates depend on the primary predictors: the nitric oxide pollution index, the sulfur dioxide pollution index, and the average annual precipitation in...
Available

Adaptive Regression Modeling of Univariate Continuous Outcomes in SAS

Knafl, George J.; Ding, Kai

Pages: 45 –62

[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....
Available

Adaptive Regression Modeling of Multivariate Continuous Outcomes

Knafl, George J.; Ding, Kai

Pages: 63 –108

[This chapter formulates and demonstrates adaptive regression modeling of means and variances for repeatedly measured continuous outcomes treated as multivariate normal. Analyses are presented of dental measurements of the distance in mm from the center of the pituitary to the pterygomaxillary...
Available

Adaptive Regression Modeling of Multivariate Continuous Outcomes in SAS

Knafl, George J.; Ding, Kai

Pages: 109 –132

[This chapter provides a description of how to use the genreg macro for adaptive regression modeling in the case of multivariate continuous outcomes treated as multivariate normally distributed. Data in wide format are often used in multivariate outcome modeling with outcome measurements under...
Available

Adaptive Transformation of Positive Valued Continuous Outcomes

Knafl, George J.; Ding, Kai

Pages: 133 –147

[This chapter presents analyses of several data sets with positive valued univariate or multivariate continuous outcomes addressing the need for power transformation of those outcomes along with power transformation of predictors for those outcomes. The outcome variables include those analyzed in...
Available

Adaptive Transformation of Positive Valued Continuous Outcomes in SAS

Knafl, George J.; Ding, Kai

Pages: 149 –157

[This chapter describes how to use the ypower macro for adaptive regression modeling accounting for fractional polynomial transformation of positive valued univariate and multivariate continuous outcomes as well as their predictors as also covered in Chap. 6. Example code and output are provided...
Available

Adaptive Logistic Regression Modeling of Univariate Dichotomous and Polytomous Outcomes

Knafl, George J.; Ding, Kai

Pages: 161 –186

[This chapter presents adaptive analyses of mercury level data, addressing how mercury level categorized as low and high (with cutoff 1.0 ppm near its median) and as low, medium, and high (with cutoffs 0.72 and 1.3 ppm near its tertiles) for n=169\documentclass[12pt]{minimal}...
Available

Adaptive Logistic Regression Modeling of Univariate Dichotomous and Polytomous Outcomes in SAS

Knafl, George J.; Ding, Kai

Pages: 187 –212

[This chapter describes how to use the genreg macro for adaptive logistic regression modeling as described in Chap. 8 and its generated output in the special case of univariate dichotomous or polytomous outcomes. Example analyses are provided for modeling means and dispersions for mercury in fish...
Available

Adaptive Logistic Regression Modeling of Multivariate Dichotomous and Polytomous Outcomes

Knafl, George J.; Ding, Kai

Pages: 213 –238

[This chapter formulates and demonstrates adaptive fractional polynomial modeling of means and dispersions for repeatedly measured dichotomous and polytomous outcomes with two or more values. Marginal modeling extends from the multivariate normal outcome context to the multivariate dichotomous...

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