BCa: BCa

In Omisc: Univariate Bootstrapping and Other Things

BCa

Description

BCa

Usage

BCa(
  boot,
  data,
  alphalower = 0.025,
  alphaupper = 0.975,
  accelleration = "jack",
  FUN,
  ...
)

Arguments

boot	A vector of bootstrap estimates of Theta
data	The data that was analyzed via the bootstrap
alphalower	The lower alpha for CI creation
alphaupper	The upper alpha for CI creation
accelleration	can currently take two values, "jack" and "bootstrap". "jack" returns the jackknife estimate of the accelleration parameter. "boot" is an experimental function that uses the bootstrap estimates in the calculation of the accelleration parameter. "boot" is many times faster (approximately n times faster where n is the number of observations).
FUN	The function used to get estimates of Theta
...	Additional arguments to FUN

Value

A matrix of BCa bootstrap CI's, the bias parameter and the accellation parameter

Examples

data<-DFSimulated()
boot<-NaiveBoot(data, groups="Rs", keepgroups=TRUE)
boot<-bootAnalysis(boot, cbind, DFanalysis, 1,2,3, robust=FALSE)
BCa(boot, data, .025,.975, accelleration="bootstrap", DFanalysis, 1,2,3, robust=FALSE)

Omisc documentation built on Aug. 9, 2022, 5:09 p.m.