Run a simmr_input object through the main simmr Markov chain Monte Carlo (MCMC) function

Source: R/simmr_mcmc.R

This is the main function of simmr. It takes a simmr_input object created via simmr_load, runs an MCMC to determine the dietary proportions, and then outputs a simmr_output object for further analysis and plotting via summary.simmr_output and plot.simmr_output.

simmr_mcmc(
  simmr_in,
  prior_control = list(means = rep(0, simmr_in$n_sources), sd = rep(1,
    simmr_in$n_sources), sigma_shape = rep(3, simmr_in$n_tracers), sigma_rate = rep(3/50,
    simmr_in$n_tracers)),
  mcmc_control = list(iter = 10000, burn = 1000, thin = 10, n.chain = 4)
)

Arguments

simmr_in

An object created via the function simmr_load

prior_control

A list of values including arguments named: means and sd which represent the prior means and standard deviations of the dietary proportions in centralised log-ratio space; shape and rate which represent the prior distribution on the residual standard deviation. These can usually be left at their default values unless you wish to include to include prior information, in which case you should use the function simmr_elicit.

mcmc_control

A list of values including arguments named iter (number of iterations), burn (size of burn-in), thin (amount of thinning), and n.chain (number of MCMC chains).

Value

An object of class simmr_output with two named top-level components:

input

The simmr_input object given to the simmr_mcmc function

output

A set of MCMC chains of class mcmc.list from the coda package. These can be analysed using the summary.simmr_output and plot.simmr_output functions.

Details

If, after running simmr_mcmc the convergence diagnostics in summary.simmr_output are not satisfactory, the values of iter, burn and thin in mcmc_control should be increased by a factor of 10.

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.

See also

simmr_load for creating objects suitable for this function, plot.simmr_input for creating isospace plots, summary.simmr_output for summarising output, and plot.simmr_output for plotting output.

Author

Andrew Parnell <andrew.parnell@mu.ie>

Examples

if (FALSE) {
## See the package vignette for a detailed run through of these 4 examples

# Data set 1: 10 obs on 2 isos, 4 sources, with tefs and concdep
data(geese_data_day1)
simmr_1 <- 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_1)

# Print
simmr_1

# MCMC run
simmr_1_out <- simmr_mcmc(simmr_1)

# Print it
print(simmr_1_out)

# Summary
summary(simmr_1_out, type = "diagnostics")
summary(simmr_1_out, type = "correlations")
summary(simmr_1_out, type = "statistics")
ans <- summary(simmr_1_out, type = c("quantiles", "statistics"))

# Plot
plot(simmr_1_out, type = "boxplot")
plot(simmr_1_out, type = "histogram")
plot(simmr_1_out, type = "density")
plot(simmr_1_out, type = "matrix")

# Compare two sources
compare_sources(simmr_1_out, source_names = c("Zostera", "Enteromorpha"))

# Compare multiple sources
compare_sources(simmr_1_out)

#####################################################################################

# A version with just one observation
data(geese_data_day1)
simmr_2 <- with(
  geese_data_day1,
  simmr_load(
    mixtures = mixtures[1, , drop = FALSE],
    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_2)

# MCMC run - automatically detects the single observation
simmr_2_out <- simmr_mcmc(simmr_2)

# Print it
print(simmr_2_out)

# Summary
summary(simmr_2_out)
summary(simmr_2_out, type = "diagnostics")
ans <- summary(simmr_2_out, type = c("quantiles"))

# Plot
plot(simmr_2_out)
plot(simmr_2_out, type = "boxplot")
plot(simmr_2_out, type = "histogram")
plot(simmr_2_out, type = "density")
plot(simmr_2_out, type = "matrix")

#####################################################################################

# Data set 2: 3 isotopes (d13C, d15N and d34S), 30 observations, 4 sources
data(simmr_data_2)
simmr_3 <- with(
  simmr_data_2,
  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
  )
)

# Get summary
print(simmr_3)

# Plot 3 times
plot(simmr_3)
plot(simmr_3, tracers = c(2, 3))
plot(simmr_3, tracers = c(1, 3))
# See vignette('simmr') for fancier axis labels

# MCMC run
simmr_3_out <- simmr_mcmc(simmr_3)

# Print it
print(simmr_3_out)

# Summary
summary(simmr_3_out)
summary(simmr_3_out, type = "diagnostics")
summary(simmr_3_out, type = "quantiles")
summary(simmr_3_out, type = "correlations")

# Plot
plot(simmr_3_out)
plot(simmr_3_out, type = "boxplot")
plot(simmr_3_out, type = "histogram")
plot(simmr_3_out, type = "density")
plot(simmr_3_out, type = "matrix")

#####################################################################################

# Data set 5 - Multiple groups Geese data from Inger et al 2006

# Do this in raw data format - Note that there's quite a few mixtures!
data(geese_data)
simmr_5 <- with(
  geese_data,
  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,
    group = groups
  )
)

# Plot
plot(simmr_5,
  xlab = expression(paste(delta^13, "C (per mille)", sep = "")),
  ylab = expression(paste(delta^15, "N (per mille)", sep = "")),
  title = "Isospace plot of Inger et al Geese data"
)

# Run MCMC for each group
simmr_5_out <- simmr_mcmc(simmr_5)

# Summarise output
summary(simmr_5_out, type = "quantiles", group = 1)
summary(simmr_5_out, type = "quantiles", group = c(1, 3))
summary(simmr_5_out, type = c("quantiles", "statistics"), group = c(1, 3))

# Plot - only a single group allowed
plot(simmr_5_out, type = "boxplot", group = 2, title = "simmr output group 2")
plot(simmr_5_out, type = c("density", "matrix"), grp = 6, title = "simmr output group 6")

# Compare sources within a group
compare_sources(simmr_5_out, source_names = c("Zostera", "U.lactuca"), group = 2)
compare_sources(simmr_5_out, group = 2)

# Compare between groups
compare_groups(simmr_5_out, source = "Zostera", groups = 1:2)
compare_groups(simmr_5_out, source = "Zostera", groups = 1:3)
compare_groups(simmr_5_out, source = "U.lactuca", groups = c(4:5, 7, 2))
}