scoringutils

Utilities for Scoring and Assessing Predictions

The scoringutils package facilitates the process of evaluating forecasts in R, using a convenient and flexible data.table-based framework. It provides broad functionality to check the input data and diagnose issues, to visualise forecasts and missing data, to transform data before scoring, to handle missing forecasts, to aggregate scores, and to visualise the results of the evaluation. The package is easily extendable, meaning that users can supply their own scoring rules or extend existing classes to handle new types of forecasts.

A good starting point are the vignettes

Details on the metrics implemented and Scoring forecasts directly. We also have several related manuscripts

and

Nikos I. Bosse, Sam Abbott, Anne Cori, Edwin van Leeuwen, Johannes Bracher* and Sebastian Funk* (*: equal contribution) (2023). Scoring epidemiological forecasts on transformed scales, PLoS Comput Biol 19(8): e1011393 https://doi.org/10.1371/journal.pcbi.1011393

for specific details on transforming forecasts for scoring.

Installation

Install the CRAN version of this package using

install.packages("scoringutils")

Install the unstable development version from GitHub using

remotes::install_github("epiforecasts/scoringutils", dependencies = TRUE)

Quick start

Forecast types

scoringutils currently supports scoring the following forecast types:

• binary: a probability for a binary (yes/no) outcome variable.
• point: a forecast for a continuous or discrete outcome variable that is represented by a single number.
• quantile: a probabilistic forecast for a continuous or discrete outcome variable, with the forecast distribution represented by a set of predictive quantiles.
• sample: a probabilistic forecast for a continuous or discrete outcome variable, with the forecast represented by a finite set of samples drawn from the predictive distribution.
• nominal categorical forecast with unordered outcome possibilities (generalisation of binary forecasts to multiple outcomes)

Input formats and input validation

The expected input format is generally a data.frame (or similar) with required columns observed, and predicted that holds the forecasts and observed values. Exact requirements depend on the forecast type. For more information, have a look at the paper, call ?as_forecast_binary, ?as_forecast_quantile etc., or have a look at the example data provided in the package (example_binary, example_point, example_quantile, example_sample_continuous, example_sample_discrete, example_nominal).

Before scoring, input data needs to be validated and transformed into a forecast object using one of the as_forecast_<type>() functions.

forecast_quantile <- example_quantile |>
  as_forecast_quantile(
    forecast_unit = c(
      "location", "forecast_date", "target_end_date", "target_type", "model", "horizon"
    )
  )
#> ℹ Some rows containing NA values may be removed. This is fine if not
#>   unexpected.

print(forecast_quantile, 2)
#> Forecast type: quantile
#> Forecast unit:
#> location, forecast_date, target_end_date, target_type, model, and horizon
#> 
#> Key: <location, target_end_date, target_type>
#>        observed quantile_level predicted location forecast_date target_end_date
#>           <num>          <num>     <int>   <char>        <Date>          <Date>
#>     1:   127300             NA        NA       DE          <NA>      2021-01-02
#>     2:     4534             NA        NA       DE          <NA>      2021-01-02
#>    ---                                                                         
#> 20544:       78          0.975       611       IT    2021-07-12      2021-07-24
#> 20545:       78          0.990       719       IT    2021-07-12      2021-07-24
#>        target_type                model horizon
#>             <char>               <char>   <num>
#>     1:       Cases                 <NA>      NA
#>     2:      Deaths                 <NA>      NA
#>    ---                                         
#> 20544:      Deaths epiforecasts-EpiNow2       2
#> 20545:      Deaths epiforecasts-EpiNow2       2

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