last updated 2/22/09,
sharing set to public
Before you begin with this code, make sure you download the latest release of OpenBUGS(now 3.0.3) and it is installed to the default directory, "C:\Program Files\OpenBUGS". We've also found that it helps to have a cleaned up registry + defregmented hard drive before using OpenBUGS. The version of R that you're using should also be noted - R 2.5.1 and 2.7.0 work great with OpenBUGS, but there was a glitch in R 2.6.0 that prevented OpenBUGS from ever running through R. When using BUGS, I think that you can obviously use WinBUGS or OpenBUGS. While both programs should yield the same result, I had much more success with OpenBUGS, particularly in terms of the program not crashing during the burn-in (anyone familiar with 'trap' error messages knows what I'm talking about).
There are 2 project files. The first (writeModel.r) is sourced by the second file (runDataCloning2.r). If you want to modify the bounds on parameters, you'll need to edit writeModel.r - otherwise, everything will be done behind the scenes. I've tried to predict many of the errors that might occur - for example, it doesn't make sense to have all sites belonging to the same group (m = 1) and an unconstrained covariance matrix.
These files won't automate every possible MSSM that you want to fit, but they will do the majority of them. The R script files actually write the BUGS code for you, so you don't have to know any BUGS coding. You should however, be aware of the priors. Uniform priors are used on SDs and growth rates - to change these, look anywhere in the file for (~ dunif(a,b), where a and b are the limits). The code also includes the option of including an interaction matrix (multiple species) or density dependence matrix (multiple populations). The priors for all of these terms are bounded (0,1).
Included are several examples of summarizing parameters, including the median, mean, and using density to estimate the mode. The actual MLEs are going to be the points associated with the best (lowest) density.
last updated 9/24/08,
sharing set to public
This project includes the WinBUGS files illustrating how to include model selection in MCMC. Variances are modeled as a mixture of source/fractionation and additional residual variance.
We include to variations on the MixBUGS model, 1 without residual variance, and 1 modeling variance as a mixture (to calculate posterior probabilities). We also include the R-script illustrating that the SIAR package likely contains a bug (even when data are generated from a 0.7-0.2-0.1 mixture, estimated contributions are nearly equal).