The threshr package deals primarily with the selection of thresholds
for use in extreme value models. It also performs predictive inferences
about future extreme values. These inferences can either be based on a
single threshold or on a weighted average of inferences from multiple
thresholds. The weighting reflects an estimated measure of the
predictive performance of the threshold and can incorporate prior
probabilities supplied by a user. At the moment only the simplest case,
where the data can be treated as independent identically distributed
observations, is considered, as described in Northrop et
al. (2017). Future releases will
tackle more general situations.
The main function in the threshr package is ithresh. It uses Bayesian
leave-one-out cross-validation to compare the extreme value predictive
ability resulting from the use of each of a user-supplied set of
thresholds. The following code produces a threshold diagnostic plot
using a dataset gom containing 315 storm peak significant waveheights.
We set a vector u_vec of thresholds; call ithresh, supplying the
data and thresholds; and use then plot the results. In this minimal
example (ithresh has further arguments) thresholds are judged in terms
of the quality of prediction of whether the validation observation lies
above the highest threshold in u_vec and, if it does, how much it
exceeds this highest threshold.
library(threshr)
u_vec_gom <- quantile(gom, probs = seq(0, 0.9, by = 0.05))
gom_cv <- ithresh(data = gom, u_vec = u_vec_gom)
plot(gom_cv)To get the current released version from CRAN:
install.packages("threshr")See vignette("threshr-vignette", package = "threshr") for an overview
of the package.