tfromx: Find thresholds from data

In EbayesThresh: Empirical Bayes Thresholding and Related Methods

Description

Given a vector of data and standard deviations (sd equals 1 for Cauchy prior), find the value or vector (heterogeneous sampling standard deviation with Laplace prior) of thresholds corresponding to the marginal maximum likelihood choice of weight.

Usage

  tfromx(x, s = 1, prior = "laplace", bayesfac = FALSE, a = 0.5,
         universalthresh = TRUE)

Arguments

x	Vector of data.
s	A single value or a vector of standard deviations if the Laplace prior is used. If a vector, must have the same length as x. Ignored if Cauchy prior is used.
prior	Specification of prior to be used; can be "cauchy" or "laplace".
bayesfac	Specifies whether Bayes factor threshold should be used instead of posterior median threshold.
a	Scale factor if Laplace prior is used. Ignored if Cauchy prior is used.
universalthresh	If universalthresh = TRUE, the thresholds will be upper bounded by universal threshold; otherwise, the thresholds can take any non-negative values.

Details

First, the routine wfromx is called to find the estimated weight. Then the routine tfromw is used to find the threshold. See the documentation for these routines for more details.

Value

The numerical value or vector of the estimated thresholds is returned.

Author(s)

Bernard Silverman

References

See ebayesthresh and http://www.bernardsilverman.com

See Also

tfromw, wfromx

Examples

tfromx(x = rnorm(100, c(rep(0,90),rep(5,10))), prior = "cauchy")

EbayesThresh documentation built on May 2, 2019, 8:36 a.m.