faraway

Table of Contents and Scripts

Zip file of R scripts

• Introduction

• Binary Response
  • Heart Disease Example
  • Logistic Regression
  • Inference
  • Diagnostics
  • Model Selection
  • Goodness of Fit
  • Estimation Problems
• Binomial and Proportion Responses
  • Binomial Regression Model
  • Inference
  • Pearson’s χ2 Statistic
  • Overdispersion
  • Quasi-Binomial
  • Beta Regression
• Variations on Logistic Regression
  • Latent Variables
  • Link Functions
  • Prospective and Retrospective Sampling
  • Prediction and Effective Doses
  • Matched Case-Control Studies
• Count Regression
  • Poisson Regression
  • Dispersed Poisson Model
  • Rate Models
  • Negative Binomial
  • Zero Inflated Count Models
• Contingency Tables
  • Two-by-Two Tables
  • Larger Two-Way Tables
  • Correspondence Analysis
  • Matched Pairs
  • Three-Way Contingency Tables
  • Ordinal Variables
• Multinomial Data
  • Multinomial Logit Model
  • Linear Discriminant Analysis
  • Hierarchical or Nested Responses
  • Ordinal Multinomial Responses
• Generalized Linear Models
  • GLM Definition
  • Fitting a GLM
  • Hypothesis Tests
  • GLM Diagnostics
  • Sandwich Estimation
  • Robust Estimation
• Other GLMs
  • Gamma GLM
  • Inverse Gaussian GLM
  • Joint Modeling of the Mean and Dispersion
  • Quasi-Likelihood GLM
  • Tweedie GLM
• Random Effects
  • Estimation
  • Inference
  • Estimating Random Effects
  • Prediction
  • Diagnostics
  • Blocks as Random Effects
  • Split Plots
  • Nested Effects
  • Crossed Effects
  • Multilevel Models
• Repeated Measures and Longitudinal Data
  • Longitudinal Data
  • Repeated Measures
  • Multiple Response Multilevel Models
• Bayesian Mixed Effect Models
  • STAN
  • INLA
  • Discussion
• Mixed Effect Models for Nonnormal Responses
  • Generalized Linear Mixed Models
  • Inference
  • Binary Response
  • Count Response
  • Generalized Estimating Equations
• Nonparametric Regression
  • Kernel Estimators
  • Splines
  • Local Polynomials
  • Confidence Bands
  • Wavelets
  • Discussion of Methods
  • Multivariate Predictors
• Additive Models
  • Modeling Ozone Concentration
  • Additive Models Using mgcv
  • Generalized Additive Models
  • Alternating Conditional Expectations
  • Additivity and Variance Stabilization
  • Generalized Additive Mixed Models
  • Multivariate Adaptive Regression Splines
• Trees
  • Regression Trees
  • Tree Pruning
  • Random Forests
  • Classification Trees
  • Classification Using Forests
• Neural Networks
  • Statistical Models as NNs
  • Feed-Forward Neural Network with One Hidden Layer
  • NN Application
  • Conclusion
• Appendix A: Likelihood Theory
• Appendix B: About R

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