Profiled Confidence Intervals {POT}

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, ...)

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.

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.

Author(s)

Mathieu Ribatet

References

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

See Also

gpd.fiscale, gpd.fishape, gpd.firl and confint

Examples

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))