by eric.ward, last updated 11/20/09, sharing set to public
This project is a variation of stable isotope mixing models. Previous approaches have fixed source parameters (mean, variance) at their MLE estimates, and proceeded to do a Bayesian analysis of the mixture of consumer diets. This assumption is fine when sample sizes are large, but often in ecology, they are small. Incorporating this uncertainty adds additional parameters, and slightly increases the total variance of mixture estimates, but has the advantage of improving mixing and reducing bias. Often, using the traditional approach may lead to multi-modal estimates of the mixture; we demonstrate that the fully Bayesian mixing model avoids this problem. A final benefit is that it allows prior information about sources to be included. The code folder contains the code to replicate the comparison done with simulated datasets.
by eric.ward, last updated 5/19/10, sharing set to public
This project is related to the stable isotope mixing models that include heterogeneity in individual diets and hierarchical nested structures (individuals in groups, groups in regions, etc). All code provided is to be used with R and either JAGS or BUGS (everything was validated using JAGS on a Mac)
by eric.ward, 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).
by brice.semmens, last updated 10/3/12, sharing set to public
MixSIR is a Bayesian isotope mixing model that incorporates uncertainty in the estimates of mix and source isotope values. The model also provides the opportunity to incorporate prior information for the proportional contribution of each source to the mix. The programs for this project are written in Matlab. I have provided the source files as well as an .exe file package so that those without Matlab can run the program. Collaborations and improvements are welcome! Prior to installing MixSIR you MUST download the (free) Matlab component runtime library and run it once on your machine. Note also that installing new versions of MixSIR will require a new download and installation of the component runtime library.
***** NOT WORKING ON YOUR NEW 64 BIT MACHINE?? *****
-- You need to install this:
you're runnning a 32-bit compiled Matlab app on a 64-bit Windows machine, and it's not finding the 32-bit Visual C++ runtime it's linked against. The Microsoft software "Microsoft Visual C++ 2005 SP1 Redistributable Package (x86)" will fix the problem. Cut and paste the link above to get the software fix!