Plot diagnostics an ithreshpred object

plot method for class "ithreshpred". Produces plots to summarise the predictive inferences made by predict.ithresh.

# S3 method for ithreshpred
plot(x, ..., ave_only = FALSE, add_best = FALSE)

Arguments

x

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

...

Additional arguments passed on to plot.evpred.

ave_only

A logical scalar. Only relevant if predict.ithresh was called with which_u = "all". If TRUE then plot only a curve for the weighted average over multiple training thresholds. If FALSE then also plot a curve for each training threshold.

add_best

A logical scalar. If TRUE then the best threshold, as judged using the validation threshold selected using the argument which_v supplied to predict.ithresh, is highlighted by plotting it with a different line style.

Value

A list containing the graphical parameters using in producing the plot including any arguments supplied via ... is returned (invisibly).

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.

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)