View source: R/PlotNumberOfInternalChanges.R
| PlotNumberOfInternalChanges | 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)
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