Bootstrap: Bootstrap

In UKFE: UK Flood Estimation

Bootstrap

Description

Resampling with replacement to approximate the sampling distribution of a statistic and quantify uncertainty.

Usage

Bootstrap(x, Stat, n = 500, Conf = 0.95, ReturnSD = FALSE, ...)

Arguments

x	a numeric vector. The sample of interest
Stat	the function (to calculate the statistic) to be applied to the bootstrapped samples. For example mean, max, or median.
n	the number of boostrapped samples (default 500). i.e. the size of the derived sampling distribution.
Conf	the confidence level of the intervals (default 0.95). Must be between 0 and 1.
ReturnSD	Logical argument with a default of FALSE. If true the bootstrapped sampling distribution is returned.
...	further arguments for the Stat function. For example if you use the GEVAM function you might want to add RP = 50 to derive a sampling distribution for the 50-year quantile.

Details

The bootstrapping procedure resamples from a sample length(x) * n times with replacement. After splitting into n samples of size length(x), the statistic of interest is calculated on each.

Value

If ReturnSD is FALSE a data.frame is returned with one row and three columns; central, lower, and upper. If ReturnSD is TRUE, the sampling distribution is returned.

Author(s)

Anthony Hammond

Examples

#Extract an AMAX sample and quantify uncertainty for the Gumbel estimated 50-year flow.
AM.203018 <- GetAM(203018)
Bootstrap(AM.203018$Flow, Stat = GumbelAM, RP = 50)
#Quantify uncertainty for the sample standard deviation at the 90 percent confidence level
Bootstrap(AM.203018$Flow, Stat = sd, Conf = 0.90)
#Return the sampling distribution of the mean and plot an associated histogram
SampDist <- Bootstrap(AM.203018$Flow, Stat = mean, ReturnSD = TRUE)
hist(SampDist)

UKFE documentation built on April 4, 2025, 12:55 a.m.