texmex (version 1.3)

gpdRangeFit: Estimate generalized Pareto distribution parameters over a range of values

Description

Estimate generalized Pareto distribution parameters over a range of values, using maximum (penalized) likelihood.

Usage

gpdRangeFit(data, umin=quantile(data, .05), umax=quantile(data, .95), nint = 10, 
            penalty = "gaussian", priorParameters = NULL, alpha=0.05,
            xlab = "Threshold", ylab = NULL, main = NULL, addNexcesses=TRUE, ...)

Arguments

data

The data vector to be modelled.

umin

The minimum threshold above which to esimate the parameters.

umax

The maximum threshold above which to esimate the parameters.

nint

The number of thresholds at which to perform the estimation.

penalty

The type of penalty to be used in the maximum penalized likelihood estimation. Should be either ``gaussian'' or ``none''. Defaults to ``gaussian''.

priorParameters

Parameters to be used for the penalty function. See the help for gpd for more informaiton.

alpha

(1 - alpha)% confidence intervals will be plotted with the point estimates. Defaults to alpha = 0.05.

xlab

Label for the x-axis.

ylab

Label for the y-axis.

main

The main title.

addNexcesses

Annotate top axis with numbers of threshold excesses arising with the corresponding values of threshold on the bottom axis.

...

Arguments to plot

Details

This is Stuart Coles' gpd.fitrange, as it appears in the ismev package, with some fairly minor changes. The function uses gpd internally and uses the default options for that function. Note this function does not extend to assessing model fit when there are covariates included in the mdoel.

See Also

gpd

Examples

par(mfrow=c(1,2))
gpdRangeFit(rain)