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Plot Calendar Ages of Changes in Rate of Sample Occurrence for Poisson Process Model

Source: R/PlotPosteriorChangePoints.R
PlotPosteriorChangePoints.Rd

Given output from the Poisson process fitting function PPcalibrate, plot the posterior density estimates for the calendar ages at which there are internal changepoints in the rate of sample occurrence \(\lambda(t)\). These density estimates are calculated conditional upon the number of internal changepoints within the period under study (which is specified as an input to the function).

Having conditioned on the number of changes, n_change, the code will extract all realisations from the the posterior of the MCMC sampler which have that number of internal changepoints in the estimate of \(\lambda(t)\). It will then provide density estimates for the (ordered) calendar ages of those internal changepoints. These density estimates are obtained using a Gaussian kernel.

Note: These graphs will become harder to interpret as the specified number of changepoints increases

For more information read the vignette:
vignette("Poisson-process-modelling", package = "carbondate")

Usage

PlotPosteriorChangePoints(
  output_data,
  n_changes = c(1, 2, 3),
  plot_cal_age_scale = "BP",
  n_burn = NA,
  n_end = NA,
  kernel_bandwidth = NA
)

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_changes

Number of internal changepoints to condition on, and plot for. A vector which can contain at most 4 elements, with values in the range 1 to 6. If not given, then c(1, 2, 3) will be used.

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.

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.

kernel_bandwidth

(Optional) The bandwidth used for the (Gaussian) kernel smoothing of the calendar age densities. If not given, then 1/50th of the overall calendar age range will be used.

Value

None

Examples

# NOTE: This example is shown with a small n_iter 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 the posterior change points for only 2 or 3 internal changes
PlotPosteriorChangePoints(pp_output, n_changes = c(2, 3))


# Changing the calendar age plotting scale to cal AD
PlotPosteriorChangePoints(pp_output, n_changes = c(2, 3),
    plot_cal_age_scale = "AD")