Given output from the Poisson process fitting function PPcalibrate calculate and plot the posterior mean rate of sample occurrence (i.e., the underlying Poisson process rate \(\lambda(t)\)) together with specified probability intervals, on a given calendar age grid (provided in cal yr BP).
Will show the original set of radiocarbon determinations (those you are modelling/summarising), the chosen calibration curve, and the estimated posterior rate of occurrence \(\lambda(t)\) on the same plot. Can also optionally show the posterior mean of each individual sample's calendar age estimate.
Note: If all you are interested in is the value of the posterior mean rate on a grid, without an accompanying plot, you can use FindPosteriorMeanRate instead.
For more information read the vignette:
vignette("Poisson-process-modelling", package = "carbondate")
PlotPosteriorMeanRate(
output_data,
n_posterior_samples = 5000,
calibration_curve = NULL,
plot_14C_age = TRUE,
plot_cal_age_scale = "BP",
show_individual_means = TRUE,
show_confidence_intervals = TRUE,
interval_width = "2sigma",
bespoke_probability = NA,
denscale = 3,
resolution = 1,
n_burn = NA,
n_end = NA,
plot_pretty = TRUE
)A list, each item containing a data frame of the calendar_age_BP, the rate_mean
and the confidence intervals for the rate - rate_ci_lower and rate_ci_upper.
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.
Number of samples it will draw, after having removed n_burn,
from the (thinned) MCMC realisations stored in output_data to estimate the
rate \(\lambda(t)\). 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.
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.
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.
(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.
(Optional) Whether to calculate and show the mean posterior
calendar age estimated for each individual \({}^{14}\)C sample on the plot as a rug on
the x-axis. Default is TRUE.
Whether to show the pointwise confidence intervals
(at chosen probability level) on the plot. Default is TRUE.
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".
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.
(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.
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.
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.
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.
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.
# 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)
# Default plot with 2 sigma interval
PlotPosteriorMeanRate(pp_output, n_posterior_samples = 100)
# Specify an 80% confidence interval
PlotPosteriorMeanRate(
pp_output,
interval_width = "bespoke",
bespoke_probability = 0.8,
n_posterior_samples = 100)
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