ashr: ashci – R documentation

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ashci

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

Methods for Adaptive Shrinkage, using Empirical Bayes

v2.2-47

GPL (>= 3)

Authors

Matthew Stephens [aut], Peter Carbonetto [aut, cre], Chaoxing Dai [ctb], David Gerard [aut], Mengyin Lu [aut], Lei Sun [aut], Jason Willwerscheid [aut], Nan Xiao [aut], Mazon Zeng [ctb]

Initial release

2020-02-19