PlotNumberOfInternalChanges {carbondate} | R Documentation |
Given output from the Poisson process fitting function PPcalibrate, plot
the posterior distribution for the number of internal changepoints in the underlying rate of
sample occurrence (i.e., in \lambda(t)
) over the period under study.
For more information read the vignette:
vignette("Poisson-process-modelling", package = "carbondate")
PlotNumberOfInternalChanges(output_data, n_burn = NA, n_end = NA)
output_data |
The return value from the updating function
PPcalibrate. Optionally, the output data can have an extra list item
named |
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_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. |
None
# 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)
PlotNumberOfInternalChanges(pp_output)