Generalized Linear Models / Generalized Estimating Equations, 2013 Edition 

Author:
 Garson, G. David 
ISBN:  9781626380158 
Publication Date:  Aug 2013 
Publisher:  Statistical Associates Publishers

Book Format:  Ebook 
List Price:  USD $5.00 
Book Description:

An introductory, graduatelevel illustrated tutorial on generalized linear models and generalized estimating equations usuing SPSS. SAS, and Stata. Covers linear regression, gamma regression, binary logistic regression, binary probit regression, Poisson regression, loglinear analysis, negative binomial regression, ordinal logistic regression, ordinal probit regression, complementary loglog regression, and other GZLM models. Also covers repeated measures linear regression, repeated...
More DescriptionAn introductory, graduatelevel illustrated tutorial on generalized linear models and generalized estimating equations usuing SPSS. SAS, and Stata. Covers linear regression, gamma regression, binary logistic regression, binary probit regression, Poisson regression, loglinear analysis, negative binomial regression, ordinal logistic regression, ordinal probit regression, complementary loglog regression, and other GZLM models. Also covers repeated measures linear regression, repeated measures binary logistic regression, and other GEE models.
Partial Table of Contents
Key Concepts and Terms 12
Types of data distributions 13
Types of link functions 19
Types of estimation methods 26
Statistical measures 26
Goodness of fit statistics 27
Likelihood ratio tests 32
Deviance ratios (scaled deviance) 33
Tests of model effects 33
Parameter estimates 34
Odds ratios 36
Pseudo Rsquare and other effect size measures 38
Contrast coefficients 39
User interfaces for GZLM 42
GZLM Models 61
Linear regression 62
Binary logistic regression 91
Binary probit regression 109
Complementary loglog (cloglog) models 118
Ordinal logistic regression 130
Ordinal probit regression 142
Gamma regression 149
Poisson regression 170
Poisson count models, rate models, and loglinear models 170
A negative binomial model as an alternative 172
Negative binomial regression 193
Mixture (Tweedie) models 200
GENERALIZED ESTIMATING EQUATIONS (GEE) 201
What is GEE? 201
Assumptions of GEE 203
Statistical packages and GEE 205
Types of GEE model 205
Subject and withinsubject variables 206
Unbalanced designs 207
The assumed (working) correlation matrix 207
Goodness of fit measures in GEE 211
Data structure for GEE 211
Data Examples 212
Repeated measures linear regression using GEE 212
Repeated measures binary logistic regression 214
Residual analysis 263
Variables available in GEE 263
Variables available in GZLM but not GEE 264
Assumptions 265
Frequently Asked Questions 267
Bibliography 286