Combine several fitted univariate frequency analyses (one per families entry, each fit to
data by a Bayesian MCMC) into a single composite frequency curve via competing-risks, mixture,
or model-averaging aggregation. Wraps the shared C++ CompositeAnalysis.
Usage
composite_analysis(
data,
families,
composite_type = "CompetingRisks",
average_method = "AIC",
sampler = "DEMCz",
iterations = 3000L,
output_length = 10000L,
credible_level = 0.9,
seed = 12345L,
exceedance_probabilities = NULL,
thinning_interval = -1L
)Arguments
- data
numeric vector of observations shared by every child analysis.
- families
character vector of child distribution family names (one child analysis each).
- composite_type
"CompetingRisks"(default),"Mixture", or"ModelAverage".- average_method
model-averaging criterion when
composite_type = "ModelAverage":"AIC"(default),"BIC","DIC","WAIC","LOOIC","Equal", or"RMSE".- 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.
0.90for 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.