simmr (version 0.4.0)

simmr: simmr: A package for fitting stable isotope mixing models via JAGS in R

Description

This package runs a simple Stable Isotope Mixing Model (SIMM) and is meant as a longer term replacement to the previous function SIAR.. These are used to infer dietary proportions of organisms consuming various food sources from observations on the stable isotope values taken from the organisms' tissue samples. However SIMMs can also be used in other scenarios, such as in sediment mixing or the composition of fatty acids. The main functions are simmr_load and simmr_mcmc. The help files contain examples of the use of this package. See also the vignette for a longer walkthrough.

Details

An even longer term replacement for properly running SIMMs is MixSIAR, which allows for more detailed random effects and the inclusion of covariates.

References

Andrew C. Parnell, Donald L. Phillips, Stuart Bearhop, Brice X. Semmens, Eric J. Ward, Jonathan W. Moore, Andrew L. Jackson, Jonathan Grey, David J. Kelly, and Richard Inger. Bayesian stable isotope mixing models. Environmetrics, 24(6):387<U+2013>399, 2013.

Andrew C Parnell, Richard Inger, Stuart Bearhop, and Andrew L Jackson. Source partitioning using stable isotopes: coping with too much variation. PLoS ONE, 5(3):5, 2010.

Examples

# NOT RUN {
# }
# NOT RUN {
# A first example with 2 tracers (isotopes), 10 observations, and 4 food sources

# Add in the data
mix = matrix(c(-10.13, -10.72, -11.39, -11.18, -10.81, -10.7, -10.54, 
-10.48, -9.93, -9.37, 11.59, 11.01, 10.59, 10.97, 11.52, 11.89, 
11.73, 10.89, 11.05, 12.3), ncol=2, nrow=10)
colnames(mix) = c('d13C','d15N')
s_names=c('Source A','Source B','Source C','Source D')
s_means = matrix(c(-14, -15.1, -11.03, -14.44, 3.06, 7.05, 13.72, 5.96), ncol=2, nrow=4)
s_sds = matrix(c(0.48, 0.38, 0.48, 0.43, 0.46, 0.39, 0.42, 0.48), ncol=2, nrow=4)
c_means = matrix(c(2.63, 1.59, 3.41, 3.04, 3.28, 2.34, 2.14, 2.36), ncol=2, nrow=4)
c_sds = matrix(c(0.41, 0.44, 0.34, 0.46, 0.46, 0.48, 0.46, 0.66), ncol=2, nrow=4)
conc = matrix(c(0.02, 0.1, 0.12, 0.04, 0.02, 0.1, 0.09, 0.05), ncol=2, nrow=4)

# Load into simmr
simmr_in = simmr_load(mixtures=mix,
                     source_names=s_names,
                     source_means=s_means,
                     source_sds=s_sds,
                     correction_means=c_means,
                     correction_sds=c_sds,
                     concentration_means = conc)

# Plot
plot(simmr_in)

# MCMC run
simmr_out = simmr_mcmc(simmr_in)

# Summary
summary(simmr_out)

# Plot
plot(simmr_out)
# }