Description Usage Arguments Details Value See Also Examples
View source: R/plot_and_summary.R
plot
method for class "ithresh". Produces an extreme value
threshold diagnostic plot based on an analysis performed by
ithresh
. Can also be used to produce a plot of
the posterior sample generated by ithresh
for a particular
training threshold.
1 2 3 4 
x 
an object of class "ithresh", a result of a call to

y 
Not used. 
... 
Additional arguments passed on to 
which_v 
A numeric scalar or vector. If If 
prob 
A logical scalar. If 
top_scale 
A logical scalar indicating Whether or not to add a scale
to the top horizontal axis. If this is added it gives the threshold on
the scale not chosen by 
add_legend 
A logical scalar indicating whether or not to add a
legend to the plot. If 
legend_pos 
The position of the legend (if required) specified using
the argument 
which_u 
Either a character scalar or a numeric scalar.
If If Otherwise, 
Produces plots of the threshold weights, defined in
equation (14) of
Northrop et al. (2017),
against training threshold. A line is produced for each of the validation
thresholds chosen in which_v
. The result is a plot like those in
the top row of Figure 7 in
Northrop et al. (2017).
If which_u
is supplied then the object with which
plot.evpost
was called is returned (invisibly).
Otherwise, a list is returned (again invisibly) with two components.
x
is a vector containing the coordinates plotted on the
(lower) horizontal axis.
y
is an length(u_vec)
by n_v
matrix of
threshold weights obtained by normalising the columns of the
matrix pred_perf
returned by ithresh
.
See equation (14) of
Northrop et al. (2017).
ithresh
for threshold selection in the i.i.d. case
based on leaveoneout crossvalidation.
summary.ithresh
Summarizing measures of threshold
predictive performance.
predict.ithresh
for predictive inference for the
largest value observed in N years.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  # [Smoother plots result from making n larger than the default n = 1000.]
# Threshold diagnostic plot
u_vec_gom < quantile(gom, probs = seq(0, 0.95, by = 0.05))
gom_cv < ithresh(data = gom, u_vec = u_vec_gom, n_v = 4)
plot(gom_cv, lwd = 2, add_legend = TRUE, legend_pos = "topleft")
mtext("significant wave height / m", side = 3, line = 2.5)
# Plot of Generalized Pareto posterior sample at the best threshold
# (based on the lowest validation threshold)
plot(gom_cv, which_u = "best")
# See which threshold was used
summary(gom_cv)
# Plot of Generalized Pareto posterior sample at the highest threshold
n_u < length(u_vec_gom)
plot(gom_cv, which_u = n_u, points_par = list(pch = 20, col = "grey"))

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