R: Solve the EBNM problem using horseshoe priors

ebnm_horseshoe {ebnm}

R Documentation

Solve the EBNM problem using horseshoe priors

Description

Solves the empirical Bayes normal means (EBNM) problem using the family of horseshoe distributions. Identical to function ebnm with argument prior_family = "horseshoe". For details about the model, see ebnm.

Usage

ebnm_horseshoe(
  x,
  s = 1,
  scale = "estimate",
  g_init = NULL,
  fix_g = FALSE,
  output = ebnm_output_default(),
  control = NULL
)

Arguments

`x`	A vector of observations. Missing observations (`NA`s) are not allowed.
`s`	A scalar specifying the standard error of the observations (observations must be homoskedastic).
`scale`	A scalar corresponding to `s\tau` in the usual parametrization of the `horseshoe` distribution, or `"estimate"` if this parameter 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 `horseshoe` or an `ebnm` object in which the fitted prior is an object of class `horseshoe`.
`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.
`control`	A list of control parameters to be passed to function `optimize`.

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 function takes parameters nsamp, the number of posterior samples to return per observation, and burn, the number of burn-in samples to discard (an MCMC sampler is used).

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.