Plot diagnostics an ithresh object

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.

Usage

# S3 method for class 'ithresh'
plot(
  x,
  y,
  ...,
  which_v = NULL,
  prob = TRUE,
  top_scale = TRUE,
  add_legend = FALSE,
  legend_pos = "topleft",
  which_u = NULL
)

Arguments

x

an object of class "ithresh", a result of a call to ithresh.

y

Not used.

...

Additional arguments passed on to matplot and/or legend and/or axis. If which_u is supplied then these arguments are passed to plot.evpost.

which_v

A numeric scalar or vector.

If which_u is not supplied (a threshold diagnostic plot is required) which_v specifies the validation thresholds, that is, the components of x$v_vec, to include in the plot.

If which_u is supplied (a plot of a posterior sample for a given threshold is required) then which_v is a numeric scalar that indicates which element of object$v_vec is used in selecting a single threshold (if which_u = "best"). Note: the default, which_v = 1 gives the lowest of the validation thresholds in object$v_vec.

prob

A logical scalar. If TRUE then the levels of thresholds are represented by the proportion of observations that lie below a threshold. If prob = FALSE then the values of the thresholds are used.

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 prob.

add_legend

A logical scalar indicating whether or not to add a legend to the plot. If method = "cv" then the legend gives the levels of the validation thresholds.

legend_pos

The position of the legend (if required) specified using the argument x in legend.

which_u

Either a character scalar or a numeric scalar. If which_u is supplied then plot.evpost is used to produce a plot of the posterior sample generated using a particular training threshold. By default a scatter plot of the posterior sample of Generalized Pareto parameters is produced.

If which_u = "best" then the training threshold achieving the largest measure of predictive performance in object$pred_perf, based on the validation threshold selected using which_v, is used. See summary.ithresh to print the best thresholds for each validation threshold.

Otherwise, which_u is a numeric scalar that selects training threshold x$u_vec[which_u]. Therefore, which_u must be an integer in 1, ..., length(x$u_vec).

Value

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).

Details

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).

It is possible that a curve on the plot may be incomplete. This indicates that, for a particular threshold level, a measure of predictive performance is -Inf. This occurs when an observation in the data lies above the estimated upper end point of the predictive distribution produced when this observation is removed.

See also

ithresh for threshold selection in the i.i.d. case based on leave-one-out cross-validation.

summary.ithresh Summarizing measures of threshold predictive performance.

print.ithresh Prints the threshold weights.

predict.ithresh for predictive inference for the largest value observed in N years.

Examples

# [Smoother plots result from making n larger than the default n = 1000.]

# Threshold diagnostic plot
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)
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)
#>        v v quantile best u best u quantile index of u_vec
#> 1 4.6070         80 3.1598              55             12
#> 2 5.1302         85 3.1598              55             12
#> 3 5.8246         90 3.6545              65             14

# 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"))