Advanced Regression Models with SAS and R

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Contents
Preface
Chapter 1 Introduction: General and Generalized Linear Regression Models
	1.1 Definition of General Linear Regression Model
	1.2 Definition of Generalized Linear Regression Model
	1.3 Parameter Estimation and Significance Test for Coefficients
	1.4 Fitted Model
	1.5 Interpretation of Estimated Regression Coefficients
	1.6 Model Goodness-of-Fit Check
	1.7 Predicted Response
	1.8 SAS Implementation
	1.9 R Implementation
	1.10 Example
		Exercises
Chapter 2 Regression Models for Response with Right-skewed Distribution
	2.1 Box-Cox Power Transformation
		2.1.1 Model Definition
		2.1.2 Fitted Model
		2.1.3 Interpretation of Estimated Regression Coefficients
		2.1.4 Predicted Response
		2.1.5 SAS Implementation
		2.1.6 R Implementation
		2.1.7 Example
	2.2 Gamma Regression Model
		2.2.1 Model Definition
		2.2.2 Fitted Model
		2.2.3 Interpretation of Estimated Regression Coefficients
		2.2.4 Predicted Response
		2.2.5 SAS Implementation
		2.2.6 R Implementation
		2.2.7 Example
		Exercises
Chapter 3 Regression Models for Binary Response
	3.1 Binary Logistic Regression Model
		3.1.1 Model Definition
		3.1.2 Fitted Model
		3.1.3 Interpretation of Estimated Regression Coefficients
		3.1.4 Predicted Probability
		3.1.5 SAS Implementation
		3.1.6 R Implementation
		3.1.7 Example
	3.2 Probit Model
		3.2.1 Model Definition
		3.2.2 Fitted Model
		3.2.3 Interpretation of Estimated Regression Coefficients
		3.2.4 Predicted Probability
		3.2.5 SAS Implementation
		3.2.6 R Implementation
		3.2.7 Example
	3.3 Complementary Log-Log Model
		3.3.1 Model Definition
		3.3.2 Fitted Model
		3.3.3 Interpretation of Estimated Regression Coefficients
		3.3.4 Predicted Probability
		3.3.5 SAS Implementation
		3.3.6 R Implementation
		3.3.7 Example
		Exercises
Chapter 4 Regression Models for Categorical Response
	4.1 Cumulative Logit Model
		4.1.1 Model Definition
		4.1.2 Fitted Model
		4.1.3 Interpretation of Estimated Regression Coefficients
		4.1.4 Predicted Probabilities
		4.1.5 SAS Implementation
		4.1.6 R Implementation
		4.1.7 Example
	4.2 Cumulative Probit Model
		4.2.1 Model Definition
		4.2.2 Fitted Model
		4.2.3 Interpretation of Estimated Regression Coefficients
		4.2.4 Predicted Probabilities
		4.2.5 SAS Implementation
		4.2.6 R Implementation
		4.2.7 Example
	4.3 Cumulative Complementary Log-Log Model
		4.3.1 Model Definition
		4.3.2 Fitted Model
		4.3.3 Interpretation of Estimated Regression Coefficients
		4.3.4 Predicted Probabilities
		4.3.5 SAS Implementation
		4.3.6 R Implementation
		4.3.7 Example
	4.4 Generalized Logit Model for Nominal Response
		4.4.1 Model Definition
		4.4.2 Fitted Model
		4.4.3 Interpretation of Estimated Regression Coefficients
		4.4.4 Predicted Probabilities
		4.4.5 SAS Implementation
		4.4.6 R Implementation
		4.4.7 Example
		Exercises
Chapter 5 Regression Models for Count Response
	5.1 Poisson Regression Model
		5.1.1 Model Definition
		5.1.2 Fitted Model
		5.1.3 Interpretation of Estimated Regression Coefficients
		5.1.4 Predicted Response
		5.1.5 SAS Implementation
		5.1.6 R Implementation
		5.1.7 Example
	5.2 Zero-truncated Poisson Regression Model
		5.2.1 Model Definition
		5.2.2 Fitted Model
		5.2.3 Interpretation of Estimated Regression Coefficients
		5.2.4 Predicted Response
		5.2.5 SAS Implementation
		5.2.6 R Implementation
		5.2.7 Example
	5.3 Zero-inflated Poisson Regression Model
		5.3.1 Model Definition
		5.3.2 Fitted Model
		5.3.3 Interpretation of Estimated Regression Coefficients
		5.3.4 Predicted Response
		5.3.5 SAS Implementation
		5.3.6 R Implementation
		5.3.7 Example
	5.4 Hurdle Poisson Regression Model
		5.4.1 Model Definition
		5.4.2 Fitted Model
		5.4.3 Interpretation of Estimated Regression Coefficients
		5.4.4 Predicted Response
		5.4.5 SAS Implementation
		5.4.6 R Implementation
		5.4.7 Example
		Exercises
Chapter 6 Regression Models for Overdispersed Count Response
	6.1 Negative Binomial Regression Model
		6.1.1 Model Definition
		6.1.2 Fitted Model
		6.1.3 Interpretation of Estimated Regression Coefficients
		6.1.4 Predicted Response
		6.1.5 SAS Implementation
		6.1.6 R Implementation
		6.1.7 Example
	6.2 Zero-truncated Negative Binomial Regression Model
		6.2.1 Model Definition
		6.2.2 Fitted Model
		6.2.3 Interpretation of Estimated Regression Coefficients
		6.2.4 Predicted Response
		6.2.5 SAS Implementation
		6.2.6 R Implementation
		6.2.7 Example
	6.3 Zero-inflated Negative Binomial Regression Model
		6.3.1 Model Definition
		6.3.2 Fitted Model
		6.3.3 Interpretation of Estimated Regression Coefficients
		6.3.4 Predicted Response
		6.3.5 SAS Implementation
		6.3.6 R Implementation
		6.3.7 Example
	6.4 Hurdle Negative Binomial Regression Model
		6.4.1 Model Definition
		6.4.2 Fitted Model
		6.4.3 Interpretation of Estimated Regression Coefficients
		6.4.4 Predicted Response
		6.4.5 SAS Implementation
		6.4.6 R Implementation
		6.4.7 Example
		Exercises
Chapter 7 Regression Models for Proportion Response
	7.1 Beta Regression Model
		7.1.1 Model Definition
		7.1.2 Fitted Model
		7.1.3 Interpretation of Estimated Regression Coefficients
		7.1.4 Predicted Response
		7.1.5 SAS Implementation
		7.1.6 R Implementation
		7.1.7 Example
	7.2 Zero-inflated Beta Regression Model
		7.2.1 Model Definition
		7.2.2 Fitted Model
		7.2.3 Interpretation of Estimated Regression Coefficients
		7.2.4 Predicted Response
		7.2.5 SAS Implementation
		7.2.6 R Implementation
		7.2.7 Example
	7.3 One-inflated Beta Regression Model
		7.3.1 Model Definition
		7.3.2 Fitted Model
		7.3.3 Interpretation of Estimated Regression Coefficients
		7.3.4 Predicted Response
		7.3.5 SAS Implementation
		7.3.6 R Implementation
		7.3.7 Example
	7.4 Zero-one-inflated Beta Regression Model
		7.4.1 Model Definition
		7.4.2 Fitted Model
		7.4.3 Interpretation of Estimated Regression Coefficients
		7.4.4 Predicted Response
		7.4.5 SAS Implementation
		7.4.6 R Implementation
		7.4.7 Example
		Exercises
Chapter 8 General Linear Regression Models for Repeated Measures Data
	8.1 Random Slope and Intercept Model
		8.1.1 Model Definition
		8.1.2 Fitted Model
		8.1.3 Interpretation of Estimated Regression Coefficients
		8.1.4 Model Goodness-of-Fit Check
		8.1.5 Predicted Response
		8.1.6 SAS Implementation
		8.1.7 R Implementation
		8.1.8 Example
	8.2 Random Slope and Intercept Model with Covariance Structure for Error
		8.2.1 Model Definition
		8.2.2 Fitted Model, Interpretation of Estimated Regression Coefficients, and Predicted Response
		8.2.3 Model Goodness-of-fit Check
		8.2.4 SAS Implementation
		8.2.5 R Implementation
		8.2.6 Example
	8.3 Generalized Estimating Equations Model
		8.3.1 Model Definition
		8.3.2 Fitted Model, Interpretation of Estimated Regression Coefficients, and Predicted Response
		8.3.3 Model Goodness-of-Fit Check
		8.3.4 SAS Implementation
		8.3.5 R Implementation
		8.3.6 Example
		Exercises
Chapter 9 Generalized Linear Regression Models for Repeated Measures Data
	9.1 Generalized Random Slope and Intercept Model
		9.1.1 Model Definition
		9.1.2 Fitted Model, Interpretation of Estimated Regression Coefficients, Model Goodness-of-Fit Check, and Predicted Response
		9.1.3 SAS Implementation
		9.1.4 R Implementation
		9.1.5 Example
	9.2 Generalized Estimating Equations Model
		9.2.1 Model Definition
		9.2.2 SAS Implementation
		9.2.3 R Implementation
		9.2.4 Example
		Exercises
Chapter 10 Hierarchical Regression Model
	10.1 Hierarchical Regression Model for Normal Response
		10.1.1 Model Definition
		10.1.2 Fitted Model, Interpretation of Estimated Regression Coefficients, Model Goodness-of-Fit Check, Predicted Response
		10.1.3 SAS Implementation
		10.1.4 R Implementation
		10.1.5 Example
	10.2 Hierarchical Regression Model for Non-normal Response
		10.2.1 Model Definition
		10.2.2 Fitted Model
		10.2.3 Interpretation of Estimated Regression Coefficients
		10.2.4 Model Goodness-of-Fit Check
		10.2.5 Predicted Response
		10.2.6 SAS Implementation
		10.2.7 R Implementation
		10.2.8 Example
		Exercises
Recommended Books
List of Notation
Subject Index