Obtain posterior quantiles using a fitted EBNM model

The quantile method for class ebnm. Quantiles for posterior distributions \(\theta_i \mid x_i, s_i, g\) are estimated via sampling. By default, ebnm does not return a posterior sampler; one can be added to the ebnm object using function ebnm_add_sampler.

# S3 method for class 'ebnm'
quantile(
  x,
  probs = seq(0, 1, 0.25),
  names = TRUE,
  type = 7,
  digits = 7,
  nsim = 1000,
  ...
)

Arguments

x: The fitted ebnm object.
probs: numeric vector of probabilities with values in \([0,1]\). (Values up to 2e-14 outside that range are accepted and moved to the nearby endpoint.)
names: logical; if true, the result has a names attribute. Set to FALSE for speedup with many probs.
type: An integer between 1 and 9 selecting one of the nine quantile algorithms detailed in quantile to be used.
digits: used only when names is true: the precision to use when formatting the percentages. In R versions up to 4.0.x, this had been set to max(2, getOption("digits")), internally.
nsim: The number of samples to use to estimate quantiles.
...: Additional arguments to be passed to the posterior sampler function. Since ebnm_horseshoe returns an MCMC sampler, it takes parameter burn, the number of burn-in samples to discard. At present, no other samplers take any additional parameters.

Value

A matrix with columns giving quantiles for each posterior \(\theta_i \mid x_i, s_i, g\).