boott {bootstrap}  R Documentation 
See Efron and Tibshirani (1993) for details on this function.
boott(x,theta, ..., sdfun=sdfunboot, nbootsd=25, nboott=200,
VS=FALSE, v.nbootg=100, v.nbootsd=25, v.nboott=200,
perc=c(.001,.01,.025,.05,.10,.50,.90,.95,.975,.99,.999))
x 
a vector containing the data. Nonparametric bootstrap sampling is used. To bootstrap from more complex data structures (e.g. bivariate data) see the last example below. 
theta 
function to be bootstrapped. Takes 
... 
any additional arguments to be passed to 
sdfun 
optional name of function for computing standard
deviation of 
nbootsd 
The number of bootstrap samples used to estimate the
standard deviation of 
nboott 
The number of bootstrap samples used to estimate the
distribution of the bootstrap T statistic.
200 is a bare minimum and 1000 or more is needed for
reliable 
VS 
If 
v.nbootg 
The number of bootstrap samples used to estimate the
variance stabilizing transformation g.
Only used if 
v.nbootsd 
The number of bootstrap samples used to estimate the
standard deviation of 
v.nboott 
The number of bootstrap samples used to estimate the
distribution of
the bootstrap T statistic. Only used if 
perc 
Confidence points desired. 
list with the following components:
confpoints 
Estimated confidence points 
theta , g 

call 
The deparsed call 
Tibshirani, R. (1988) "Variance stabilization and the bootstrap". Biometrika (1988) vol 75 no 3 pages 43344.
Hall, P. (1988) Theoretical comparison of bootstrap confidence intervals. Ann. Statisi. 16, 150.
Efron, B. and Tibshirani, R. (1993) An Introduction to the Bootstrap. Chapman and Hall, New York, London.
# estimated confidence points for the mean
x < rchisq(20,1)
theta < function(x){mean(x)}
results < boott(x,theta)
# estimated confidence points for the mean,
# using variancestabilization bootstrapT method
results < boott(x,theta,VS=TRUE)
results$confpoints # gives confidence points
# plot the estimated var stabilizing transformation
plot(results$theta,results$g)
# use standard formula for stand dev of mean
# rather than an inner bootstrap loop
sdmean < function(x, ...)
{sqrt(var(x)/length(x))}
results < boott(x,theta,sdfun=sdmean)
# 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 boot 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 < boott(1:n,theta, xdata)