# Run a `simmr_input` object through the main simmr Markov chain Monte Carlo (MCMC) function 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`. ```r 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). ## Returns 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. ## Examples ```r ## Not run: ## 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)) ## End(Not run) ``` ## 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(s) Andrew Parnell

simmr_mcmc function