If you find any errors or have any questions, please send emails to (junxu dot soc at gmail dot com). I will respond at my earliest convenience. Or, you can fill out the publicly shared excel sheet for errata registration toward the end of this page. For corrections and revisions, please see the corrigenda page. Thank you for your help and support!

News and Updates of Important R Packages/Functions 

• The VGAM package (by Thomas Yee) is versatile in estimating a wide variety of vector generalized linear and additive models.

• The Zelig package (by Gary King and his colleagues) is deprecated and replaced by the clarify package (by Gary King and his colleagues), which is a great R package for post-estimation analysis. Here is a document to reproduce the examples in my MARs book using functions from clarify. As this document is written, clarify still doesn't work with accelerated failure time survival regression models, in part due to the issues with the marginaleffects package.  

• The marginaleffects (the counterpart of margins in Stata) package (by Vincent Arel-Bundock) is amazing for post-estimation analysis. Documents to show how the examples using margins and other functions in MARs can be replaced by marginaleffects (to come soon).

• The ggeffects (by Daniel Lüdecke) and effects (by John Fox) packages are great at graphing quantities of interest (e.g., response variables, predicted prob, rates) after estimation.

Chapter 1 Introduction and Linear Regression

• R Materials

  • R Scripts (OLS Regression, R Lab, Bayes OLS)

  • Selected Important R functions and packages

    • cut: turn numeric to factor variables 

    • tab1 (the epiDisplay package): display one-way tabulation

    • tabpct (the epiDisplay package): construct two-way tabulation with automatic mosaic plot 

    • glm and lm (the stats package): fit linear models

    • ggpredict (the ggeffects package): estimate marginal means and adjusted predictions

    • bayesglm (the arm package): Bayesian generalized linear models

Chapter 2 Binary Regression

• R Materials

  • R Scripts (Binary Regression, Bayes Binary Regression)

  • Selected Important R functions and packages

    • PseudoR2 (the DescTools package): compute commonly used pseudo R-squareds

    • prediction and performance (the ROCR package): compute and draw ROC

    • dc (the glm.predict package): compute predicted values and discrete changes

    • dydx and margins (the margins package): compute marginal effect of a given variable

    • bayesglm (the arm package): Bayesian generalized linear models

    • MCMClogit (the MCMCpack package): Markov chain Monte Carlo for logistic regression

Chapter 3 Polytomous Regression

• R Materials

  • R Scripts (Polytomous Regression, Bayes Polytomous Regression)

  • Selected Important R functions and packages

    • star.cumulative and star.nomial (the EffectStar package) and effectstars.vglm (the EffectStar2 package): estimate cumulative logit and multinomial logit models and draw star plots for regression coefficients/odds ratio coefficients, 

    • vglm and rrvglm (the VGAM package): estimate vector generalized linear model and reduced-rank vector generalized linear models

    • get_predicted (the insight package, the easystats package): estimate model predictions and their confidence intervals

    • multinom (the nnet package): estimate multinomial regression models

    • mnp (the MNP package): estimate multinomial probit models

    • stan_polr (the rstanarm package): estimate Bayesian proportional odds models

Chapter 4 Count Regression

• R Materials

  • R Scripts (Count Regression, Bayes Count Regression)

  • Selected Important R functions and packages

    • glm (stats): estimate Poisson regression models

    • glm.nb (MASS): estimate negative binomial regression models

    • hurdle (countreg): estimate hurdle models

    • zeroinfl (countreg): estimate zero-inflated Poisson and negative binomial regression models

Chapter 5 Survival Regression

• R Materials

  • R Scripts (Survival Regression, Bayes Survival Regression)

  • Selected Important R functions and packages

    • suvfit (survival): compute Kaplan-Meier (KM) estimates

    • suvreg (survival): estimate AFT (accelerated failure-time) survival regression models

    • coxph (survival): estimate Cox PH regression models

Chapter 6 Extension

• Multilevel Modeling (R Scripts)

• Causal Inference (R Scripts)

• Machine Learning (R Scripts)

Supplementary Materials

• Corrigenda

  • Please fill out this form if you find any errors

• Data

  • galileo1632.RData (Chapter 1: The Galileo 1632 data)

  • gssCum7212Teach.dta (Chapters 1-4: Random sample from the General Social Survey data)

  • hkFisher.Rdata (Chapter 4: The von Bortkiewicz Prusian horse kick data trimmed by R. A. Fisher)

  • gssndi7802rec.dta (Chapter 5: The recoded random sample of the General Social Survey (GSS) -- National Death Index linked cumulative data file 1978-2002)

  • indcntdta01Samp.dta (Chapter 6.1 Multilevel Regression: A random sample of the BRFSS 2010 Survey data with both individual and county data linked/indcntdta01.dta) 

• Marginal effects formulas

• Epigraph (for those interested in the original Chinese texts of the epigraphs at the beginning of each chapter)

Presentation, Seminar, and Workshops

• 2023 ASA Workshop (slides, lab guide)