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2016 | OriginalPaper | Chapter

9. Adaptive Logistic Regression Modeling of Univariate Dichotomous and Polytomous 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 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 categorized into the dichotomous levels of high and low and into the polytomous levels of high, medium, and low in terms of weight and length of the fish and the river in which they were caught. Adaptive ordinal and multinomial regression for polytomous outcomes are demonstrated. Residual analyses based on continuous predictors, like weight and length, of dichotomous and polytomous outcomes are better conducted using grouped data. Formulations and example analyses are provided for grouped-data residual analyses of both dichotomous and polytomous outcomes.

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Literature
go back to reference Allison, P. (2012). Logistic regression using SAS: Theory and applications (2nd ed.). Cary, NC: SAS Institute. Allison, P. (2012). Logistic regression using SAS: Theory and applications (2nd ed.). Cary, NC: SAS Institute.
go back to reference SAS Institute. (1995). Logistic regression examples using the SAS system. Cary, NC: SAS Institute. SAS Institute. (1995). Logistic regression examples using the SAS system. Cary, NC: SAS Institute.
go back to reference SAS Institute. (2004). SAS/STAT 9.1 user’s guide. Cary, NC: SAS Institute. SAS Institute. (2004). SAS/STAT 9.1 user’s guide. Cary, NC: SAS Institute.
go back to reference Stokes, M. E., Davis, C. S., & Koch, G. G. (2012). Categorical data analysis using the SAS system (3rd ed.). Cary, NC: SAS Institute. Stokes, M. E., Davis, C. S., & Koch, G. G. (2012). Categorical data analysis using the SAS system (3rd ed.). Cary, NC: SAS Institute.
go back to reference Zelterman, D. (2002). Advanced log-linear models using SAS. Cary, NC: SAS Institute. Zelterman, D. (2002). Advanced log-linear models using SAS. Cary, NC: SAS Institute.
Metadata
Title
Adaptive Logistic Regression Modeling of Univariate Dichotomous and Polytomous Outcomes in SAS
Authors
George J. Knafl
Kai Ding
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-33946-7_9

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