The confint method for class ebnm.
Estimates posterior "credible intervals" for each "true mean" \(\theta_i\).
We define the \((1 - \alpha)\)% credible interval for \(\theta_i\) as
the narrowest continuous interval \([a_i, b_i]\) such that
\(\theta_i \in [a_i, b_i]\) with posterior probability at least
\(1 - \alpha\), where \(\alpha \in (0,1)\). We estimate these credible
intervals using Monte Carlo sampling. Note
that 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 ebnm
confint(object, parm, level = 0.95, nsim = 1000, ...)The fitted ebnm object.
A vector of numeric indices specifying which means \(\theta_i\) are to be given confidence intervals. If missing, all observations are considered.
The "confidence level" \(1 - \alpha\) desired.
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.
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".