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Plot Number of Calendar Age Clusters Estimated in Bayesian Non-Parametric DPMM Output

Source: R/PlotNumberOfClusters.R
PlotNumberOfClusters.Rd

Given output from one of the Bayesian non-parametric summarisation functions (either PolyaUrnBivarDirichlet or WalkerBivarDirichlet) plot the estimated number of calendar age clusters represented by the \({}^{14}\)C samples.

For more information read the vignette:
vignette("Non-parametric-summed-density", package = "carbondate")

Usage

PlotNumberOfClusters(output_data, n_burn = NA, n_end = NA)

Arguments

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_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.

Value

None

See also

PlotPredictiveCalendarAgeDensity and PlotCalendarAgeDensityIndividualSample for more plotting functions using DPMM output.

Examples

# NOTE: these examples are shown with a small n_iter to speed up execution.
# When you run ensure n_iter gives convergence (try function default).

polya_urn_output <- PolyaUrnBivarDirichlet(
    two_normals$c14_age,
    two_normals$c14_sig,
    intcal20,
    n_iter = 500,
    n_thin = 2,
    show_progress = FALSE)

PlotNumberOfClusters(polya_urn_output)