plot_predictions {scoringutils}

Plot Predictions vs True Values

Description

Make a plot of observed and predicted values

Usage

plot_predictions(data, by = NULL, x = "date", range = c(0, 50, 90))

Arguments

data

a data.frame that follows the same specifications outlined in score(). To customise your plotting, you can filter your data using the function make_NA().

by

character vector with column names that denote categories by which the plot should be stratified. If for example you want to have a facetted plot, this should be a character vector with the columns used in facetting (note that the facetting still needs to be done outside of the function call)

x

character vector of length one that denotes the name of the variable

range

numeric vector indicating the interval ranges to plot. If 0 is included in range, the median prediction will be shown.

Value

ggplot object with a plot of true vs predicted values

Examples

library(ggplot2)
library(magrittr)

example_continuous %>%
  make_NA (
    what = "truth",
    target_end_date >= "2021-07-22",
    target_end_date < "2021-05-01"
  ) %>%
  make_NA (
    what = "forecast",
    model != "EuroCOVIDhub-ensemble",
    forecast_date != "2021-06-07"
  ) %>%
  plot_predictions (
    x = "target_end_date",
    by = c("target_type", "location"),
    range = c(0, 50, 90, 95)
  ) +
  facet_wrap(~ location + target_type, scales = "free_y") +
  aes(fill = model, color = model)

example_continuous %>%
  make_NA (
    what = "truth",
    target_end_date >= "2021-07-22",
    target_end_date < "2021-05-01"
  ) %>%
  make_NA (
    what = "forecast",
    forecast_date != "2021-06-07"
  ) %>%
  plot_predictions (
    x = "target_end_date",
    by = c("target_type", "location"),
    range = c(0)
  ) +
  facet_wrap(~ location + target_type, scales = "free_y") +
  aes(fill = model, color = model)