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Bayesian point-process (peaks-over-threshold) frequency analysis

Source: R/analysis.R
point_process_analysis.Rd

Fit a peaks-over-threshold point-process model with a Bayesian MCMC analysis. Wraps the shared C++ PointProcessAnalysis.

Usage

point_process_analysis(
  data,
  threshold = NULL,
  total_years = NULL,
  sampler = "DEMCz",
  iterations = 3000L,
  output_length = 10000L,
  credible_level = 0.9,
  seed = 12345L,
  exceedance_probabilities = NULL,
  thinning_interval = -1L
)

Arguments

data

numeric vector of peak observations.

threshold

optional numeric threshold (when NULL, the model default cascade is used).

total_years

optional numeric exposure length in years.

sampler

MCMC sampler: "DEMCz" (default), "DEMCzs", "ARWMH", or "NUTS".

iterations

number of post-warmup MCMC iterations.

output_length

number of posterior samples used to build the credible band.

credible_level

credible-interval width (e.g. 0.90 for a 90% band).

seed

PRNG seed for the sampler (fixed for reproducibility).

exceedance_probabilities

optional numeric vector of exceedance probabilities at which to tabulate the curve; when NULL, the 25 standard default ordinates are used.

thinning_interval

MCMC thinning interval; -1 (default) keeps the sampler's own default.

Value

A named list with the same shape as mixture_analysis().

Examples

x <- c(950, 1020, 1130, 1250, 1090, 1430, 1180, 1620, 1300, 1550, 1210)
fit <- point_process_analysis(x, threshold = 900, total_years = 20, iterations = 100,
                              output_length = 200, seed = 12345, thinning_interval = 1)
fit$dic
#> [1] 202.4186