distLexBoot: Bootstrapping uncertainty intervals for return periods

Description

Calculates and plots bootstrap uncertainty intervals for plotLextreme.

Usage

distLexBoot(dlf, nbest = 3, selection = NULL, n = 100, prop = 0.8, conf.lev = 0.95, RPs = NULL, log = TRUE, progbars = TRUE, ...)

Arguments

dlf

dlf object, as returned by distLextreme, is passed to plotLextreme.

nbest

Number of best fitted distribution functions in dlf for which bootstrapping is to be done. Overriden by selection. DEFAULT: 3

selection

Character vector with distribution function names to be used. Suggested to keep this low. DEFAULT: NULL

Number of subsamples to be processed (computing time increases extraordinarily). DEFAULT: 100

prop

Proportion of sample to be used in each run. DEFAULT: 0.8

conf.lev

Confidence level (Proportion of subsamples within 'confidence interval'). Quantiles extracted from this value are passed to quantileMean. DEFAULT: 0.95

RPs

Return Period vector, by default calculated internally based on value of log. DEFAULT: NULL

log

RPs suitable for plot on a logarithmic axis? DEFAULT: TRUE

progbars

Show progress bar for Monte Carlo simulation? DEFAULT: TRUE

...

Further arguments passed to distLquantile like truncate, quiet=TRUE

Value

invisible dlf object, see printL. Additional elements are: exBootSelection (names of distributions), exBootRPs (x values for plot) exBootSim (all simulation results) and exBootCI (agregated into CI band). The last two are each a list with a matrix (RPs)

Details

Has not been thoroughly tested yet. Bootstrapping defaults can probably be improved.

Examples

Run this code


data(annMax)
dlf <- distLextreme(annMax, selection=c("wak","gum","gev","nor"))
dlfB <- distLexBoot(dlf, nbest=4, conf.lev=0.5, n=10) # n low for quick example tests
plotLexBoot(dlfB)