[This article was first published on Gianluca Baio's blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
I finally (nearly) got around to prepare the R code to replicate the examples in the book. I divided the examples by chapter and then linked to the R scripts and, for those involving Bayesian analysis, the associated JAGS models.Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
At the moment, the scripts basically cover 3 running examples (discussed in several parts of the book):
- MCMC.R. A Gibbs sampler for the very simple case of a semi-conjugated Normal model \begin{eqnarray*}y_i &\sim& \mbox{Normal}(\mu,\sigma^2)\\ \mu\mid\sigma^2 & \sim & \mbox{Normal}(\mu_0,\sigma^2_0) \\ \tau=1/\sigma^2 &\sim& \mbox{Gamma}(\alpha_0,\beta_0)\end{eqnarray*} to show the basics of the method. That’s all written in R and I also provide some additional code to make some plots, showing convergence varying the number of iterations, something like this:
- normalModel.R. A linear regression model \begin{eqnarray*} y_i & \sim & \mbox{Normal}(\mu_i,\sigma^2) \\ \mu_i & = & \alpha + \beta x_i \\ \alpha,\beta & \sim & \mbox{Normal}(0,h^2) \\ \log(\sigma) & \sim & \mbox{Uniform}(-k,k) \end{eqnarray*} This has several slightly different specifications to show how things change when centring a covariate, or selecting a longer burn-in for the MCMC, or using thinning. There are actually two versions of this script and this modifies the original model to include blocking to improve convergence and estimate the predictive distribution. Both versions also include the JAGS code to run the different models.
- HEexample.R. This runs a (reasonably simple) health economic model whose aim is to estimate the cost-effectiveness of a new chemotherapy drug. This actually consists of two steps. The first one specifies the model in terms of a set of relevant parameters. These are estimated through MCMC (and the JAGS code is provided). Then, using BCEA, the actual cost-effectiveness analysis is run. The script provides the code to run the basic analysis, as well as more advanced ones (including the computation of the Expected Value of Partial Information, model average to account for uncertainty in model specification, decision-maker’s risk aversion and the mixed analysis to consider non-optimal market configurations $-$ I’ve discussed this here).
To leave a comment for the author, please follow the link and comment on their blog: Gianluca Baio's blog.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.