Creates a plot showing how the estimate of a high quantile in the tail of a dataset based on the GPD approximation varies with threshold or number of extremes.
quant(data, p = 0.99, models = 30, start = 15, end = 500, reverse =
TRUE, ci = 0.95, auto.scale = TRUE, labels = TRUE, …)numeric vector of data
desired probability for quantile estimate (e.g. 0.99 gives 99th percentile)
number of consecutive gpd models to be fitted
lowest number of exceedances to be considered
maximum number of exceedances to be considered
should plot be by increasing threshold
(TRUE) or number of extremes (FALSE)
probability for asymptotic confidence band; for no confidence band set to zero
whether or not plot should be automatically scaled; if not, xlim and ylim graphical parameters may be entered
whether or not axes should be labelled
other graphics parameters
A table of results is returned invisibly.
For every model gpd is called. Evaluation may be slow.
Confidence intervals by the Wald method (which is fastest).
# NOT RUN {
data(danish)
# }
# NOT RUN {
quant(danish, 0.999)
# }
# NOT RUN {
# Estimates of the 99.9th percentile of the Danish losses using
# the GPD model with various thresholds
# }
Run the code above in your browser using DataLab