bootstrap {bootstrap} | R Documentation |
See Efron and Tibshirani (1993) for details on this function.
bootstrap(x,nboot,theta,..., func=NULL)
x |
a vector containing the data. To bootstrap more complex data structures (e.g. bivariate data) see the last example below. |
nboot |
The number of bootstrap samples desired. |
theta |
function to be bootstrapped. Takes |
... |
any additional arguments to be passed to |
func |
(optional) argument specifying the functional the distribution of thetahat that is desired. If func is specified, the jackknife after-bootstrap estimate of its standard error is also returned. See example below. |
list with the following components:
thetastar |
the |
func.thetastar |
the functional |
jack.boot.val |
the jackknife-after-bootstrap values for |
jack.boot.se |
the jackknife-after-bootstrap standard error
estimate of |
call |
the deparsed call |
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. (1992) Jackknife-after-bootstrap standard errors and influence functions. J. Roy. Stat. Soc. B, vol 54, pages 83-127
Efron, B. and Tibshirani, R. (1993) An Introduction to the Bootstrap. Chapman and Hall, New York, London.
# 100 bootstraps of the sample mean # (this is for illustration; since "mean" is a # built in function, bootstrap(x,100,mean) would be simpler!) x <- rnorm(20) theta <- function(x){mean(x)} results <- bootstrap(x,100,theta) # as above, but also estimate the 95th percentile # of the bootstrap dist'n of the mean, and # its jackknife-after-bootstrap standard error perc95 <- function(x){quantile(x, .95)} results <- bootstrap(x,100,theta, func=perc95) # To bootstrap 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 bootstrap the vector 1,2,..n. # For example, to bootstrap # 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 <- bootstrap(1:n,20,theta,xdata)