confint.ebnm

The <code><a href="/link/confint?package=ebnm&version=1.1-2" data-mini-rdoc="ebnm::confint">confint</a></code> method for class <code>ebnm</code>.
 Estimates the highest posterior density (HPD) intervals by sampling from
 the posterior. By default, <code>ebnm</code> does not return a posterior
 sampler; one can be added to the <code>ebnm</code> object using function
 <code>ebnm_add_sampler</code>.

Provides simple, fast, and stable functions to fit the normal
means model using empirical Bayes. For available models and details, see
function ebnm(). A detailed introduction to the package is provided
by Willwerscheid and Stephens (2021) <arXiv:2110.00152>.

Peter Carbonetto

ebnm

Solve the Empirical Bayes Normal Means Problem

Jason Willwerscheid

Matthew Stephens

Andrew Goldstein

Yusha Liu

confint.ebnm function

<dl><dt>object</dt>
<dd>The fitted <code>ebnm</code> object.</dd>
<dt>parm</dt>
<dd>A vector of numeric indices specifying which means \(\theta_i\)
are to be given confidence intervals. If missing, all observations are
considered.</dd>
<dt>level</dt>
<dd>The confidence level required.</dd>
<dt>nsim</dt>
<dd>The number of samples to use to estimate confidence intervals.</dd>
<dt>...</dt>
<dd>Additional arguments to be passed to the posterior sampler
function. Since <code>ebnm_horseshoe</code> returns an MCMC sampler, it takes
parameter <code>burn</code>, the number of burn-in samples to discard. At
present, no other samplers take any additional parameters.</dd></dl>

Arguments

The <code><a href='https://rdrr.io/r/stats/confint.html'>confint</a></code> method for class <code>ebnm</code>.
 Estimates the highest posterior density (HPD) intervals by sampling from
 the posterior. By default, <code>ebnm</code> does not return a posterior
 sampler; one can be added to the <code>ebnm</code> object using function
 <code>ebnm_add_sampler</code>.

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

<dl>

<dt>object</dt>
<dd>The fitted <code>ebnm</code> object.</dd>


<dt>parm</dt>
<dd>A vector of numeric indices specifying which means \(\theta_i\)
are to be given confidence intervals. If missing, all observations are
considered.</dd>


<dt>level</dt>
<dd>The confidence level required.</dd>


<dt>nsim</dt>
<dd>The number of samples to use to estimate confidence intervals.</dd>


<dt>...</dt>
<dd>Additional arguments to be passed to the posterior sampler
function. Since <code>ebnm_horseshoe</code> returns an MCMC sampler, it takes
parameter <code>burn</code>, the number of burn-in samples to discard. At
present, no other samplers take any additional parameters.</dd>

</dl>

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

Description

Usage

Value

Arguments