confint.ebnm: Obtain confidence intervals using a fitted EBNM model

In ebnm: Solve the Empirical Bayes Normal Means Problem

Obtain confidence intervals using a fitted EBNM model

Description

The confint method for class ebnm. Estimates the highest posterior density (HPD) intervals by sampling from the posterior. By default, ebnm does not return a posterior sampler; one can be added to the ebnm object using function ebnm_add_sampler.

Usage

## S3 method for class 'ebnm'
confint(object, parm, level = 0.95, nsim = 1000, ...)

Arguments

object	The fitted ebnm object.
parm	A vector of numeric indices specifying which means \theta_i are to be given confidence intervals. If missing, all observations are considered.
level	The confidence level required.
nsim	The number of samples to use to estimate confidence intervals.
...	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 lower and upper confidence limits for each mean \theta_i. These will be labelled as "CI.lower" and "CI.upper".

ebnm documentation built on Oct. 13, 2023, 1:16 a.m.