Profiled Confidence Intervals: Profiled Confidence interval for the GP Distribution

Description

Compute profiled confidence intervals on parameter and return level for the GP distribution. This is achieved through the profile likelihood procedure.

Usage

gpd.pfshape(object, range, xlab, ylab, conf = 0.95, nrang = 100,
vert.lines = TRUE, ...)
gpd.pfscale(object, range, xlab, ylab, conf = 0.95, nrang = 100,
vert.lines = TRUE, ...)
gpd.pfrl(object, prob, range, thresh, xlab, ylab, conf = 0.95, nrang =
100, vert.lines = TRUE, ...)

Value

Returns a vector of the lower and upper bound for the profile confidence interval. Moreover, a graphic of the profile likelihood function is displayed.

Arguments

object: R object given by function fitgpd.
prob: The probability of non exceedance.
range: Vector of dimension two. It gives the lower and upper bound on which the profile likelihood is performed.
thresh: Optional. The threshold. Only needed with non constant threshold.
xlab, ylab: Optional Strings. Allows to label the x-axis and y-axis. If missing, default value are considered.
conf: Numeric. The confidence level.
nrang: Numeric. It specifies the number of profile likelihood computed on the whole range range.
vert.lines: Logical. If TRUE (the default), vertical lines are plotted.
...: Optional parameters to be passed to the plot function.

Author

Mathieu Ribatet

References

Coles, S. (2001). An Introduction to Statistical Modelling of Extreme Values. Springer Series in Statistics. London.

Examples

Run this code

data(ardieres)
events <- clust(ardieres, u = 4, tim.cond = 8 / 365,
clust.max = TRUE)
MLE <- fitgpd(events[, "obs"], 4, 'mle')
gpd.pfshape(MLE, c(0, 0.8))
rp2prob(10, 2)
gpd.pfrl(MLE, 0.95, c(12, 25))

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