Generalized Linear Models / Generalized Estimating Equations, 2013 Edition

An introductory, graduate-level 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, log-linear analysis, negative binomial regression, ordinal logistic regression, ordinal probit regression, complementary log-log regression, and other GZLM models. Also covers repeated measures linear regression, repeated...
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An introductory, graduate-level 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, log-linear analysis, negative binomial regression, ordinal logistic regression, ordinal probit regression, complementary log-log 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 R-square 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 log-log (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 within-subject 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