ebnm_point_laplace | R Documentation |
Solves the empirical Bayes normal means (EBNM) problem using the family of
point-Laplace priors (the family of mixtures where one component is a point
mass at \mu
and the other is a double-exponential distribution
centered at \mu
). Identical to function ebnm
with argument
prior_family = "point_laplace"
. For details about the model, see
ebnm
.
ebnm_point_laplace(
x,
s = 1,
mode = 0,
scale = "estimate",
g_init = NULL,
fix_g = FALSE,
output = ebnm_output_default(),
optmethod = NULL,
control = NULL
)
x |
A vector of observations. Missing observations ( |
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 |
scale |
A scalar specifying the scale parameter of the Laplace
component or |
g_init |
The prior distribution |
fix_g |
If |
output |
A character vector indicating which values are to be returned.
Function |
optmethod |
A string specifying which optimization function is to be
used. Options include |
control |
A list of control parameters to be passed to the
optimization function specified by parameter |
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 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
.
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