ebnm_point_exponential: Solve the EBNM problem using point-exponential priors

In ebnm: Solve the Empirical Bayes Normal Means Problem

Solve the EBNM problem using point-exponential priors

Description

Solves the empirical Bayes normal means (EBNM) problem using the family of point-exponential priors (the family of mixtures where one component is a point mass at \mu and the other is a (nonnegative) exponential distribution with mode \mu). Identical to function ebnm with argument prior_family = "point_exponential". For details about the model, see ebnm.

Usage

ebnm_point_exponential(
  x,
  s = 1,
  mode = 0,
  scale = "estimate",
  g_init = NULL,
  fix_g = FALSE,
  output = ebnm_output_default(),
  optmethod = NULL,
  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	A scalar specifying the mode of the prior g or "estimate" if the mode is to be estimated from the data.
scale	A scalar specifying the scale parameter of the exponential component or "estimate" if the scale is to be estimated from the data.
g_init	The prior distribution g. Usually this is left unspecified (NULL) and estimated from the data. However, it can be used in conjuction with fix_g = TRUE to fix the prior (useful, for example, to do computations with the "true" g in simulations). If g_init is specified but fix_g = FALSE, g_init specifies the initial value of g used during optimization. When supplied, g_init should be an object of class gammamix or an ebnm object in which the fitted prior is an object of class gammamix.
fix_g	If TRUE, fix the prior g at g_init instead of estimating it.
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.
optmethod	A string specifying which optimization function is to be used. Options include "nlm", "lbfgsb" (which calls optim with method = "L-BFGS-B"), and "trust" (which calls into package trust). Other options are "nohess_nlm", "nograd_nlm", and "nograd_lbfgsb", which use numerical approximations rather than exact expressions for the Hessian and (for the latter two) the gradient. The default option is "nohess_nlm".
control	A list of control parameters to be passed to the optimization function specified by parameter optmethod.

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.