simmr (version 0.5.1.215)

simmr: simmr: A package for fitting stable isotope mixing models via JAGS and FFVB 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, simmr_mcmc, and simmr_ffvb. The help files contain examples of the use of this package. See also the vignette for a longer walkthrough.

Author

Andrew Parnell <[e-mail]>

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):387–399, 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

# \donttest{
# A first example with 2 tracers (isotopes), 10 observations, and 4 food sources
data(geese_data_day1)
simmr_in <- with(
  geese_data_day1,
  simmr_load(
    mixtures = mixtures,
    source_names = source_names,
    source_means = source_means,
    source_sds = source_sds,
    correction_means = correction_means,
    correction_sds = correction_sds,
    concentration_means = concentration_means
  )
)

# Plot
plot(simmr_in)

# MCMC run
simmr_out <- simmr_mcmc(simmr_in)

# Check convergence - values should all be close to 1
summary(simmr_out, type = "diagnostics")

# Look at output
summary(simmr_out, type = "statistics")

# Look at influence of priors
prior_viz(simmr_out)

# Plot output
plot(simmr_out, type = "histogram")
# }