Fit GPDs to various thresholds and plot the fitted GPD shape as a function of the threshold.
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, ...)
numeric
vector of data.
numeric
vector of thresholds for which
to fit a GPD to the excesses.
logical
indicating whether
confidence intervals are to be computed.
confidence level of the confidence intervals if
estimate.cov
.
x-axis label.
y-axis label (if NULL
, a default is used).
label of the secondary x-axis.
logical
indicating whether a plot is produced.
additional arguments passed to the underlying
plot()
.
Invisibly returns a list
containing the thresholds
considered, the corresponding excesses and the fitted GPD
objects as returned by the underlying fit_GPD_MLE()
.
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).
# NOT RUN {
set.seed(271)
X <- rt(1000, df = 3.5)
GPD_shape_plot(X)
# }
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