Plot discrimination for binary forecasts — plot

Visualise the discrimination ability of binary forecasts by plotting the distribution of predicted probabilities, stratified by the observed outcome. A well-discriminating model will show clearly separated distributions for the two observed levels.

Usage

plot_discrimination(forecast, type = c("histogram", "density"), ...)

Arguments

forecast: A forecast_binary object (see as_forecast_binary()).
type: Character, either "histogram" (default) or "density". "histogram" shows a histogram with proportions on the y-axis; "density" shows kernel density curves.
...: Additional arguments passed to ggplot2::geom_histogram() or ggplot2::geom_density(), depending on type. For example, bins or binwidth for histograms, or bw and adjust for density plots.

Value

A ggplot object showing the distribution of predicted probabilities, coloured by observed outcome level.

Examples

library(ggplot2)
forecast <- as_forecast_binary(na.omit(example_binary))

plot_discrimination(forecast)
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.


plot_discrimination(forecast, type = "density")


plot_discrimination(forecast, bins = 10)


plot_discrimination(forecast) +
  facet_wrap(~model)
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.