gpd.prof: Profile Log-likelihoods for Stationary GPD Models

In ismev: An Introduction to Statistical Modeling of Extreme Values

Description

Produce profile log-likelihoods for shape parameters and m year/block return levels for stationary GPD models using the output of the function gpd.fit.

Usage

gpd.prof(z, m, xlow, xup, npy = 365, conf = 0.95, nint = 100)
gpd.profxi(z, xlow, xup, conf = 0.95, nint = 100)

Arguments

z	An object returned by gpd.fit. The object should represent a stationary model.
m	The return level (i.e.\ the profile likelihood is for the value that is exceeded with probability 1/m).
xlow, xup	The least and greatest value at which to evaluate the profile likelihood.
npy	The number of observations per year.
conf	The confidence coefficient of the plotted profile confidence interval.
nint	The number of points at which the profile likelihood is evaluated.

Value

A plot of the profile likelihood is produced, with a horizontal line representing a profile confidence interval with confidence coefficient conf.

See Also

gpd.fit, gpd.diag

Examples

data(rain)
rnfit <- gpd.fit(rain, 10)
## Not run: gpd.prof(rnfit, m = 10, 55, 75)
## Not run: gpd.profxi(rnfit, -0.02, 0.15)

Example output

Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-28. For overview type 'help("mgcv-package")'.
$threshold
[1] 10

$nexc
[1] 2003

$conv
[1] 0

$nllh
[1] 6123.465

$mle
[1] 7.43768624 0.05045225

$rate
[1] 0.1142547

$se
[1] 0.23606472 0.02256649

ismev documentation built on May 1, 2019, 9:10 p.m.