Learn R Programming
texmex (version 2.1)

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)
"print"(x, ...)
"summary"(object, ...)
"plot"(x, xlab = "Threshold", ylab = NULL, main = NULL, addNexcesses=TRUE, ...)

Arguments

data
The data vector to be modelled.
umin
The minimum threshold above which to estimate the parameters.
umax
The maximum threshold above which to estimate 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 evm for more informaiton.
alpha
100(1 - alpha)% confidence intervals will be plotted with the point estimates. Defaults to alpha = 0.05.
x, object
Arguments to print and summary functions.
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, refactored into a function that does the computations, and method functions. The function uses evm 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 model.

See Also

evm

Examples

Run this code
par(mfrow=c(1,2))
plot(gpdRangeFit(rain))

Run the code above in your browser using DataLab