Bayesian Mixing Models in R
mixsiar
Process mixing model output from JAGS
Combine sources from a finished MixSIAR model (a posteriori)
Compare the predictive accuracy of 2 or more MixSIAR models
Load trophic discrimination factor (TDF) data
Load mixture data
Calculate the normalized surface area of the source convex hull
Load source data
Plot proportions by a continuous covariate
Plot biotracer data
Plot biotracer data (1-D)
Plot biotracer data (2-D)
Plot posterior uncertainty intervals from a MixSIAR model
Plot prior
Run the JAGS model
Summary statistics from posterior of MixSIAR model
Write the JAGS model file
Creates and runs Bayesian mixing models to analyze biological tracer data (i.e. stable isotopes, fatty acids), which estimate the proportions of source (prey) contributions to a mixture (consumer). 'MixSIAR' is not one model, but a framework that allows a user to create a mixing model based on their data structure and research questions, via options for fixed/ random effects, source data types, priors, and error terms. 'MixSIAR' incorporates several years of advances since 'MixSIR' and 'SIAR'.