| Description |
1 online resource (239 p.) |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- List of Figures -- List of Tables -- Acknowledgments -- 1 Introduction -- Motivation -- Audience -- Coverage and Organization -- Note -- 2 Introduction to R Studio and Packages -- Objects in R -- Packages -- The Catregs Package -- Conclusion -- Notes -- 3 Overview of OLS Regression and Introduction to the Generalized Linear Model -- The OLS Regression Model -- Regression Estimates -- Estimating Regression Models in R -- Interpreting Regression Models with Graphs -- Diagnostics -- The Generalized Linear Model (GLM) |
|
The GLM and Link Functions -- Iteratively Reweighted Least Squares (IRLS) -- Maximum Likelihood (ML) Estimation -- Model Comparison in the Generalized Linear Model -- 4 Describing Categorical Variables and Some Useful Tests of Association -- Univariate Distributions -- Bivariate Distributions -- Log-Linear Models -- Summary -- Notes -- 5 Regression for Binary Outcomes -- Relationship to the Linear Probability and Log-Linear Models -- The Statistical Approach: Link Functions -- The Latent Variable Approach -- Estimating a BRM in R -- Wald Tests -- LR Tests -- Linear Combinations -- Interpretation |
|
Regression Coefficients -- Odds Ratios -- Predicted Probabilities -- Average Marginal Effects -- Diagnostics -- Residuals -- Influential Cases -- Measures of Fit -- 6 Regression for Binary Outcomes -- Moderation and Squared Terms -- Moderation -- Categorical × Categorical -- Categorical × Continuous -- Continuous × Continuous -- Squared Terms -- Notes -- 7 Regression for Ordinal Outcomes -- Generalizing the BRM -- Estimating an ORM in R -- Interpretation -- Regression Coefficients -- Odds Ratios -- Predicted Probabilities -- Average and Conditional Marginal Effects |
|
The Parallel Regression Assumption -- Partial Proportional Odds Models -- Nominal and Binary Models -- Note -- 8 Regression for Nominal Outcomes -- The Multinomial Regression Model and Its Assumptions -- Estimating the MRM -- Interpreting the MRM -- Regression Coefficients -- Odds Ratios -- Predicted Probabilities -- Marginal Effects -- Combining Categories -- The Independence of Irrelevant Alternatives -- 9 Regression for Count Outcomes -- Poisson Regression -- Estimation -- Incidence Rate Ratios -- Marginal Effects -- Predicted Probabilities -- Negative Binomial Regression -- Estimation |
|
Incidence Rate Ratios -- Marginal Effects -- Predicted Probabilities -- Zero-Inflated Models -- Estimation -- Interpretation -- Comparing Count Models -- Truncated Counts -- Estimation and Interpretation -- Hurdle Models -- Estimation and Interpretation -- Note -- 10 Additional Outcome Types -- Conditional or Fixed Effects Logistic Regression -- Travel Choice -- Occupational Preferences -- Rank-Ordered or Exploded Logistic Regression -- Summary -- 11 Special Topics: Comparing between Models and Missing Data -- Comparing between Models -- The KHB Method -- Comparing Marginal Effects |
| Summary |
This book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). It focuses on graphic displays of results as these are a core strength of using R for statistical analysis, and uses statistical models which are relevant to the social sciences |
| Notes |
Description based upon print version of record |
|
Missing Data |
| Form |
Electronic book
|
| Author |
Doan, Long
|
| ISBN |
9781000907865 |
|
1000907864 |
|
