Linear and linear mixed-effects models in R and WinBUGS / JAGS

Thu 02 February 2012 by Adrian Brasoveanu

Plan for the Feb. 7, Feb. 9 and probably Feb. 14 classes (again, feel free to bring your laptops to class and work through the scripts on your own as we go through them in class):

  1. First script: bayes-winbugs-jags-3.r; contents: - simple linear regression (simulated data, R analysis, WinBUGS / JAGS analysis)

    • goodness-of-fit assessment in Bayesian analyses (posterior predictive distributions and Bayesian p-values)
    • interpretation of confidence vs. credible intervals
    • fixed-effects 1-way ANOVA (simulated data, R analysis, WinBUGS / JAGS analysis)
    • random-effects 1-way ANOVA (simulated data, R analysis, WinBUGS / JAGS analysis)
    • inferring binomial proportions with hierarchical priors (random-effects for “coins”, i.e., basically, random-effect “binomial” ANOVA)
  2. Second script: bayes-winbugs-jags-4.r; contents:

    • 2-way ANOVA w/o and w/ interactions (simulated data, R analysis, WinBUGS / JAGS analysis)
    • ANCOVA and the importance of covariate standardization (simulated data, R analysis, WinBUGS / JAGS analysis)
    • linear mixed-effects models—-random intercepts only, independent random intercepts and slopes, correlated random intercepts and slopes (simulated data, R analysis, WinBUGS / JAGS analysis)