ebnm_ash: Solve the EBNM problem using an ash family of distributions

In ebnm: Solve the Empirical Bayes Normal Means Problem

Solve the EBNM problem using an ash family of distributions

Description

A wrapper to function ash in package ashr. Identical to function ebnm with argument prior_family = "ash".

Usage

ebnm_ash(
  x,
  s = 1,
  mode = 0,
  scale = "estimate",
  g_init = NULL,
  fix_g = FALSE,
  output = ebnm_output_default(),
  control = NULL,
  ...
)

Arguments

x	A vector of observations. Missing observations (NAs) are not allowed.
s	A vector of standard errors (or a scalar if all are equal). Standard errors may not be exactly zero, and missing standard errors are not allowed.
mode	Passed to ash as parameter mode.
scale	Passed to ash as parameter mixsd.
g_init	Passed to ash as parameter g.
fix_g	Passed to ash as parameter fixg.
output	A character vector indicating which values are to be returned. Function ebnm_output_default() provides the default return values, while ebnm_output_all() lists all possible return values. See Value below.
control	Passed to ash as parameter control.
...	Additional parameters to be passed to ash.

Value

An ebnm object. Depending on the argument to output, the object is a list containing elements:

data

A data frame containing the observations x and standard errors s.

posterior

A data frame of summary results (posterior means, standard deviations, second moments, and local false sign rates).

fitted_g

The fitted prior \hat{g}.

log_likelihood

The optimal log likelihood attained, L(\hat{g}).

posterior_sampler

A function that can be used to produce samples from the posterior. The sampler takes a single parameter nsamp, the number of posterior samples to return per observation.

S3 methods coef, confint, fitted, logLik, nobs, plot, predict, print, quantile, residuals, simulate, summary, and vcov have been implemented for ebnm objects. For details, see the respective help pages, linked below under See Also.

See Also

See ebnm for examples of usage and model details.

Available S3 methods include coef.ebnm, confint.ebnm, fitted.ebnm, logLik.ebnm, nobs.ebnm, plot.ebnm, predict.ebnm, print.ebnm, print.summary.ebnm, quantile.ebnm, residuals.ebnm, simulate.ebnm, summary.ebnm, and vcov.ebnm.

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