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, berry-b@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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.