plot.ithreshpred {threshr} | R Documentation |
Plot diagnostics an ithreshpred object
Description
plot
method for class "ithreshpred"
. Produces plots to
summarise the predictive inferences made by predict.ithresh
.
Usage
## S3 method for class 'ithreshpred'
plot(x, ..., ave_only = FALSE, add_best = FALSE)
Arguments
x |
an object of class |
... |
Additional arguments passed on to
|
ave_only |
A logical scalar. Only relevant if
|
add_best |
A logical scalar. If |
Details
Single threshold case, where
predict.ithresh
was called with numeric scalar
which_u
or which_u = "best"
.
plot.evpred
is called to produce the plot.
Multiple threshold case, where
predict.ithresh
was called with which_u = "all"
.
Again, plot.evpred
is called but now the
estimated predictive distribution function (type = "p"
used
in the call to predict.ithresh
) or density function
(type = "d"
) is plotted for each of the training thresholds
(grey lines) as is the result of the weighted average over the
different training thresholds (black line).
If graphical parameters, such as lty
, lwd
or col
are passed via ...
then the first element relates to the
weighted average and the remaining length(x$u_vec)
elements to
the respective training thresholds in u_vec
.
Value
A list containing the graphical parameters using in producing the plot including any arguments supplied via ... is returned (invisibly).
See Also
ithresh
for threshold selection in the i.i.d. case
based on leave-one-out cross-validation.
predict.ithresh
for predictive inference for the
largest value observed in N years.
plot.ithresh
for the S3 plot method for objects of
class ithresh
.
summary.ithresh
Summarizing measures of threshold
predictive performance.
Examples
u_vec_gom <- quantile(gom, probs = seq(0, 0.9, by = 0.05))
gom_cv <- ithresh(data = gom, u_vec = u_vec_gom, n_v = 3)
# Note: gom_cv$npy contains the correct value of npy (it was set in the
# call to ithresh, via attr(gom, "npy").
# If object$npy doesn't exist then the argument npy must be supplied
# in the call to predict().
### Best training threshold based on the lowest validation threshold
# Predictive distribution function
npy_gom <- length(gom)/105
best_p <- predict(gom_cv, n_years = c(100, 1000))
plot(best_p)
# Predictive density function
best_d <- predict(gom_cv, type = "d", n_years = c(100, 1000))
plot(best_d)
### All thresholds plus weighted average of inferences over all thresholds
# Predictive distribution function
all_p <- predict(gom_cv, which_u = "all")
plot(all_p)
# Predictive density function
all_d <- predict(gom_cv, which_u = "all", type = "d")
plot(all_d)
### ... and highlight the best threshold
plot(all_p, add_best = TRUE)
plot(all_d, add_best = TRUE)