qrmtools (version 0.0-10)

GPD_shape_plot: Fitted GPD Shape as a Function of the Threshold

Description

Fit GPDs to various thresholds and plot the fitted GPD shape as a function of the threshold.

Usage

GPD_shape_plot(x, thresholds = seq(quantile(x, 0.5), quantile(x, 0.99),
                                   length.out = 65),
               estimate.cov = TRUE, conf.level = 0.95,
               lines.args = list(lty = 2), xlab = "Threshold", ylab = NULL,
               xlab2 = "Excesses", plot = TRUE, ...)

Arguments

x

numeric vector of data.

thresholds

numeric vector of thresholds for which to fit a GPD to the excesses.

estimate.cov

logical indicating whether confidence intervals are to be computed.

conf.level

confidence level of the confidence intervals if estimate.cov.

lines.args

list of arguments passed to the underlying lines() for drawing the confidence intervals.

xlab

x-axis label.

ylab

y-axis label (if NULL, a default is used).

xlab2

label of the secondary x-axis.

plot

logical indicating whether a plot is produced.

…

additional arguments passed to the underlying plot().

Value

Invisibly returns a list containing the thresholds considered, the corresponding excesses and the fitted GPD objects as returned by the underlying fit_GPD_MLE().

Details

Such plots can be used in the peaks-over-threshold method for determining the optimal threshold (as the smallest after which the plot is (roughly) stable).

Examples

# NOT RUN {
data(fire)
GPD_shape_plot(fire)
abline(v = c(10, 20), lty = 3) # possible threshold choices from mean_excess_plot()
# }