bootCI: Pointwise confidence intervals by bootstrap
In extremefit: Estimation of Extreme Conditional Quantiles and Probabilities

Description Usage Arguments Details Value See Also Examples

Pointwise quantiles and survival probabilities confidence intervals using bootstrap.

bootCI(X, weights = rep(1, length(X)), probs = 1:(length(X) -
  1)/length(X), xgrid = sort(X), B = 100, alpha = 0.05,
  type = "quantile", CritVal = 10, initprop = 1/10, gridlen = 100,
  r1 = 1/4, r2 = 1/20, plot = F)

`X`	a numeric vector of data values.
`weights`	a numeric vector of weights.
`probs`	used if type = "quantile", a numeric vector of probabilities with values in [0,1].
`xgrid`	used if type = "survival", a numeric vector with values in the domain of X.
`B`	an integer giving the number of bootstrap iterations.
`alpha`	the type 1 error of the bootstrap (1-alpha)-confidence interval.
`type`	type is either "quantile" or "survival".
`CritVal`	a critical value associated to the kernel function given by `CriticalValue`. The default value is 10 corresponding to the rectangular kernel.
`gridlen, initprop, r1, r2`	parameters used in the function hill.adapt (see `hill.adapt`).
`plot`	If `TRUE`, the bootstrap confidence interval is plotted.

Generate B samples of X with replacement to estimate the quantiles of orders probs or the survival probability corresponding to xgrid. Determine the bootstrap pointwise (1-alpha)-confidence interval for the quantiles or the survival probabilities.

`LowBound`	the lower bound of the bootstrap (1-alpha)-confidence interval.
`UppBound`	the upper bound of the bootstrap (1-alpha)-confidence interval of level.

hill.adapt,CriticalValue,predict.hill.adapt

X <- abs(rcauchy(400))
hh <- hill.adapt(X)
probs <- probgrid(0.1, 0.999999, length = 100)
B <- 200
## Not run:  #For computing time purpose
  bootCI(X, weights = rep(1, length(X)), probs = probs, B = B, plot = TRUE)
  xgrid <- sort(sample(X, 100))
  bootCI(X, weights = rep(1, length(X)), xgrid = xgrid, type = "survival", B = B, plot = TRUE)

## End(Not run)