- output_data
The return value from one of the Bayesian non-parametric DPMM functions, e.g.
PolyaUrnBivarDirichlet or
WalkerBivarDirichlet, or a list, each item containing
one of these return values. Optionally, the output data can have an extra list item
named label which is used to set the label on the plot legend.
- n_posterior_samples
Number of samples it will draw, after having removed n_burn,
from the (thinned) realisations stored in the DPMM outputs to estimate the
predictive calendar age density. These samples may be repeats if the number of, post burn-in,
realisations is less than n_posterior_samples. If not given, 5000 is used.
- 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
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.
- show_SPD
Whether to calculate and show the summed probability
distribution on the plot (optional). Default is FALSE.
- show_confidence_intervals
Whether to show the pointwise confidence intervals
(at the chosen probability level) on the plot. Default is TRUE.
- interval_width
The confidence intervals to show for both the
calibration curve and the predictive density. 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
Whether to scale the vertical range of the summarised calendar age density plot
relative to the calibration curve plot (optional). Default is 3 which means
that the maximum predictive density will be at 1/3 of the height of the plot.
- resolution
The distance between calendar ages at which to calculate the predictive shared
density. These ages will be created on a regular grid that automatically covers the
calendar period of the given set of \({}^{14}\)C samples. 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 given to
PolyaUrnBivarDirichlet or WalkerBivarDirichlet)
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.