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.