plot_wis: Plot Contributions to the Weighted Interval Score

In scoringutils: Utilities for Scoring and Assessing Predictions

Plot Contributions to the Weighted Interval Score

Description

Visualise the components of the weighted interval score: penalties for over-prediction, under-prediction and for high dispersion (lack of sharpness)

Usage

plot_wis(scores, x = "model", relative_contributions = FALSE, flip = FALSE)

Arguments

scores	A data.frame of scores based on quantile forecasts as produced by score() and summarised using summarise_scores()
x	The variable from the scores you want to show on the x-Axis. Usually this will be "model".
relative_contributions	show relative contributions instead of absolute contributions. Default is FALSE and this functionality is not available yet.
flip	boolean (default is FALSE), whether or not to flip the axes.

Value

A ggplot2 object showing a contributions from the three components of the weighted interval score

References

Bracher J, Ray E, Gneiting T, Reich, N (2020) Evaluating epidemic forecasts in an interval format. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008618

Examples

library(ggplot2)
scores <- score(example_quantile)
scores <- summarise_scores(scores, by = c("model", "target_type"))

plot_wis(scores,
  x = "model",
  relative_contributions = TRUE
) +
  facet_wrap(~target_type)
plot_wis(scores,
  x = "model",
  relative_contributions = FALSE
) +
  facet_wrap(~target_type, scales = "free_x")

scoringutils documentation built on Feb. 16, 2023, 7:30 p.m.