Plot Individual Realisations of Posterior Rate of Sample Occurrence for Poisson Process Model — PlotRateIndividualRealisation • carbondate

Given output from the Poisson process fitting function PPcalibrate plot individual realisations from the MCMC for the rate of sample occurrence (i.e., realisations of the underlying Poisson process rate \(\lambda(t)\)), on a given calendar age grid (provided in cal yr BP). Specify either n_realisations if you want to select a random set of realisations, or realisations if you want to provide a vector of specific realisations.

Usage

PlotRateIndividualRealisation(
  output_data,
  n_realisations = 10,
  plot_realisations_colour = NULL,
  realisations = NULL,
  calibration_curve = NULL,
  plot_14C_age = TRUE,
  plot_cal_age_scale = "BP",
  interval_width = "2sigma",
  bespoke_probability = NA,
  denscale = 3,
  resolution = 1,
  n_burn = NA,
  n_end = NA,
  plot_pretty = TRUE
)

Arguments

output_data: The return value from the updating function PPcalibrate. Optionally, the output data can have an extra list item named label which is used to set the label on the plot legend.
n_realisations: Number of randomly sampled realisations to be drawn from MCMC posterior and plotted. Default is 10.
plot_realisations_colour: The colours to be used to plot the individual realisations. Default is greyscale (otherwise should have same length as number of realisations).
realisations: Specific indices of realisations (in thinned version) to plot if user does not want to sample realisations randomly). If specified will override n_realisations.
calibration_curve: This is usually not required since the name of the calibration curve variable is saved in the output data. However, if the variable with this name is no longer in your environment then you should pass the calibration curve here. If provided, this should be a dataframe which should contain at least 3 columns entitled calendar_age, c14_age and c14_sig. This format matches intcal20.
plot_14C_age: Whether to use the radiocarbon age (\({}^{14}\)C yr BP) as the units of the y-axis in the plot. Defaults to TRUE. If FALSE uses F\({}^{14}\)C concentration instead.
plot_cal_age_scale: (Optional) The calendar scale to use for the x-axis. Allowed values are "BP", "AD" and "BC". The default is "BP" corresponding to plotting in cal yr BP.
interval_width: The confidence intervals to show for the calibration curve. Choose from one of "1sigma" (68.3%), "2sigma" (95.4%) and "bespoke". Default is "2sigma".
bespoke_probability: The probability to use for the confidence interval if "bespoke" is chosen above. E.g., if 0.95 is chosen, then the 95% confidence interval is calculated. Ignored if "bespoke" is not chosen.
denscale: (Optional) Whether to scale the vertical range of the Poisson process mean rate plot relative to the calibration curve plot. Default is 3 which means that the maximum of the mean rate will be at 1/3 of the height of the plot.
resolution: The distance between calendar ages at which to calculate the value of the rate \(\lambda(t)\). These ages will be created on a regular grid that automatically covers the calendar period specified in output_data. Default is 1.
n_burn: The number of MCMC iterations that should be discarded as burn-in (i.e., considered to be occurring before the MCMC has converged). This relates to the number of iterations (n_iter) when running the original update functions (not the thinned output_data). Any MCMC iterations before this are not used in the calculations. If not given, the first half of the MCMC chain is discarded. Note: The maximum value that the function will allow is n_iter - 100 * n_thin (where n_iter and n_thin are the arguments that were given to PPcalibrate) which would leave only 100 of the (thinned) values in output_data.
n_end: The last iteration in the original MCMC chain to use in the calculations. Assumed to be the total number of iterations performed, i.e. n_iter, if not given.
plot_pretty: logical, defaulting to TRUE. If set TRUE then will select pretty plotting margins (that create sufficient space for axis titles and rotates y-axis labels). If FALSE will implement current user values.

Value

None

Examples

#' # NOTE: All these examples are shown with a small n_iter and n_posterior_samples
# to speed up execution.
# Try n_iter and n_posterior_samples as the function defaults.

pp_output <- PPcalibrate(
    pp_uniform_phase$c14_age,
    pp_uniform_phase$c14_sig,
    intcal20,
    n_iter = 1000,
    show_progress = FALSE)

# Plot 10 random realisations in greyscale
PlotRateIndividualRealisation(
    pp_output,
    n_realisations = 10)


# Plot three random realisations with specific colours
PlotRateIndividualRealisation(
    pp_output,
    n_realisations = 3,
    plot_realisations_colour = c("red", "green", "purple"))


# Plot some specific realisations
PlotRateIndividualRealisation(
    pp_output,
    realisations = c(60, 73, 92),
    plot_realisations_colour = c("red", "green", "purple"))