distLexBoot  R Documentation 
Calculates and plots bootstrap uncertainty intervals for plotLextreme
.
distLexBoot( dlf, nbest = 3, selection = NULL, n = 100, prop = 0.8, conf.lev = 0.95, replace = FALSE, RPs = NULL, log = TRUE, progbars = TRUE, quiet = FALSE )
dlf 

nbest 
Number of best fitted distribution functions in dlf for which
bootstrapping is to be done. Overridden by 
selection 
Character vector with distribution function names to be used. Suggested to keep this low. DEFAULT: NULL 
n 
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

replace 
Logical: replace in each 
RPs 
Return Period vector, by default calculated internally based on
value of 
log 
RPs suitable for plot on a logarithmic axis? DEFAULT: TRUE 
progbars 
Show progress bar for Monte Carlo simulation? DEFAULT: TRUE 
quiet 
Logical: suppress messages? See 
Has not been thoroughly tested yet. Bootstrapping defaults can probably be improved.
invisible dlf object, see printL
.
Additional elements are: exBootCL (confidence level),
exBootRPs (x values for plot)
exBootSim (all simulation results) and exBootCI (aggregated into CI band).
The last two are each a list with a matrix (return levels)
Berry Boessenkool, berryb@gmx.de, Sept 2015 + Dec 2016
plotLexBoot
, distLextreme
data(annMax) dlf < distLextreme(annMax, selection=c("gum","gev","wak","nor")) dlfB < distLexBoot(dlf, nbest=4, conf.lev=0.5, n=10) # n low for quick example tests plotLexBoot(dlfB) plotLexBoot(dlfB, selection=c("nor","gev")) plotLexBoot(dlfB, selection=c("gum","gev","wak","nor"), order=FALSE)
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