postPredSPD: SPD-based Posterior Predictive Check

In nimbleCarbon: Bayesian Analyses of Radiocarbon Dates with NIMBLE

SPD-based Posterior Predictive Check

Description

Generates SPDs from posterior samples.

Usage

postPredSPD(
  x,
  errors,
  calCurve,
  model,
  a,
  b,
  params,
  nsim,
  method = NULL,
  spdnormalised = TRUE,
  datenormalised = TRUE,
  ncores = 1,
  verbose = TRUE
)

Arguments

x	a vector of observed uncalibrated radiocarbon ages.
errors	a vector of standard deviations corresponding to each estimated radiocarbon age.
calCurve	character string naming a calibration curve already provided with the rcarbon package (currently 'intcal20','intcal13','intcal13nhpine16','shcal20','shcal13','shcal13shkauri16',”marine13','marine20').
model	growth model
a	lower (earliest) limit of the distribution (in BP).
b	upper (latest) limit of the distribution (in BP).
params	list of vectors containing model parameters. The names attribute of each vector should match growth model parameters.
nsim	number of SPDs to be generated. Default is the length of the parameter vectors supplied in the argument params.
method	method for the creation of random dates from the fitted model. Either 'uncalsample' or 'calsample'.
spdnormalised	a logical variable indicating whether the total probability mass of the SPD is normalised to sum to unity for both observed and simulated data. Default is TRUE.
datenormalised	a logical variable indicating whether dates should be normalised to sum to unity or not. Default is TRUE.
ncores	number of cores used for for parallel execution. Default is 1.
verbose	a logical variable indicating whether extra information on progress should be reported. Default is TRUE.

Value

An object of class spdppc with the following elements

• obs A data.frame containing the years (in Cal BP) and the corresponding summed probability in the observed data.

• spdmat A matrix containing the summed probability distribution of the simulated data.

nimbleCarbon documentation built on Aug. 14, 2023, 5:08 p.m.