ashci: Credible Interval Computation for the ash object

In ashr: Methods for Adaptive Shrinkage, using Empirical Bayes

Credible Interval Computation for the ash object

Description

Given the ash object returned by the main function ash, this function computes a posterior credible interval (CI) for each observation. The ash object must include a data component to use this function (which it does by default).

Usage

ashci(
  a,
  level = 0.95,
  betaindex,
  lfsr_threshold = 1,
  tol = 0.001,
  trace = FALSE
)

Arguments

a	the fitted ash object
level	the level for the credible interval, (default=0.95)
betaindex	a vector consisting of locations of betahat where you would like to compute the credible interval
lfsr_threshold	a scalar, if specified then computes CIs only for observations more significant than that threshold.
tol	passed to uniroot; indicates desired accuracy.
trace	a logical variable denoting whether some of the intermediate results of iterations should be displayed to the user. Default is FALSE.

Details

Uses uniroot to find credible interval, one at a time for each observation. The computation cost is linear in number of observations.

Value

A matrix, with 2 columns, ith row giving CI for ith observation

Examples

beta = c(rep(0,20),rnorm(20))
sebetahat = abs(rnorm(40,0,1))
betahat = rnorm(40,beta,sebetahat)
beta.ash = ash(betahat, sebetahat)

CImatrix=ashci(beta.ash,level=0.95)

CImatrix1=ashci(beta.ash,level=0.95,betaindex=c(1,2,5))
CImatrix2=ashci(beta.ash,level=0.95,lfsr_threshold=0.1)

ashr documentation built on Aug. 22, 2023, 1:07 a.m.