bootstrap (version 2019.6)

bcanon: Nonparametric BCa Confidence Limits

Description

See Efron and Tibshirani (1993) for details on this function.

Usage

bcanon(x, nboot, theta, ..., 
       alpha=c(0.025, 0.05, 0.1, 0.16, 0.84, 0.9, 0.95, 0.975))

Arguments

x

a vector containing the data. To bootstrap more complex data structures (e.g. bivariate data) see the last example below.

nboot

number of bootstrap replications

theta

function defining the estimator used in constructing the confidence points

...

additional arguments for theta

alpha

optional argument specifying confidence levels desired

Value

list with the following components

confpoints

estimated bca confidence limits

z0

estimated bias correction

acc

estimated acceleration constant

u

jackknife influence values

call

The deparsed call

References

Efron, B. and Tibshirani, R. (1986). The Bootstrap Method for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical Science, Vol 1., No. 1, pp 1-35.

Efron, B. (1987). Better bootstrap confidence intervals (with discussion). J. Amer. Stat. Assoc. vol 82, pg 171

Efron, B. and Tibshirani, R. (1993) An Introduction to the Bootstrap. Chapman and Hall, New York, London.

Examples

# NOT RUN {
#  bca limits for the  mean 
#  (this is for illustration; 
#   since "mean" is a built in function,
#   bcanon(x,100,mean) would be simpler!)
   x <- rnorm(20)                
   theta <- function(x){mean(x)}
   results <- bcanon(x,100,theta)   
                              
# To obtain bca limits for functions of more 
# complex data structures, write theta
# so that its argument x is the set of observation
# numbers and simply pass as data to bcanon 
# the vector 1,2,..n. 
# For example, find bca limits for
# the correlation coefficient from a set of 15 data pairs:
   xdata <- matrix(rnorm(30),ncol=2)
   n <- 15
   theta <- function(x,xdata){ cor(xdata[x,1],xdata[x,2]) }
   results <- bcanon(1:n,100,theta,xdata)
# }