Create a forecast object for binary forecasts — as_forecast

Process and validate a data.frame (or similar) or similar with forecasts and observations. If the input passes all input checks, those functions will be converted to a forecast object. A forecast object is a data.table with a class forecast and an additional class that depends on the forecast type.

The arguments observed, predicted, etc. make it possible to rename existing columns of the input data to match the required columns for a forecast object. Using the argument forecast_unit, you can specify the columns that uniquely identify a single forecast (and thereby removing other, unneeded columns. See section "Forecast Unit" below for details).

Usage

as_forecast_binary(
  data,
  forecast_unit = NULL,
  observed = NULL,
  predicted = NULL
)

Arguments

data: A data.frame (or similar) with predicted and observed values. See the details section of for additional information on the required input format.
forecast_unit: (optional) Name of the columns in data (after any renaming of columns) that denote the unit of a single forecast. See get_forecast_unit() for details. If NULL (the default), all columns that are not required columns are assumed to form the unit of a single forecast. If specified, all columns that are not part of the forecast unit (or required columns) will be removed.
observed: (optional) Name of the column in data that contains the observed values. This column will be renamed to "observed".
predicted: (optional) Name of the column in data that contains the predicted values. This column will be renamed to "predicted".

Value

A forecast object of class forecast_binary

Required input

The input needs to be a data.frame or similar with the following columns:

observed: factor with exactly two levels representing the observed values. The highest factor level is assumed to be the reference level. This means that corresponding value in predicted represent the probability that the observed value is equal to the highest factor level.
predicted: numeric with predicted probabilities, representing the probability that the corresponding value in observed is equal to the highest available factor level.

For convenience, we recommend an additional column model holding the name of the forecaster or model that produced a prediction, but this is not strictly necessary.

See the example_binary data set for an example.

Forecast unit

In order to score forecasts, scoringutils needs to know which of the rows of the data belong together and jointly form a single forecasts. This is easy e.g. for point forecast, where there is one row per forecast. For quantile or sample-based forecasts, however, there are multiple rows that belong to a single forecast.

The forecast unit or unit of a single forecast is then described by the combination of columns that uniquely identify a single forecast. For example, we could have forecasts made by different models in various locations at different time points, each for several weeks into the future. The forecast unit could then be described as forecast_unit = c("model", "location", "forecast_date", "forecast_horizon"). scoringutils automatically tries to determine the unit of a single forecast. It uses all existing columns for this, which means that no columns must be present that are unrelated to the forecast unit. As a very simplistic example, if you had an additional row, "even", that is one if the row number is even and zero otherwise, then this would mess up scoring as scoringutils then thinks that this column was relevant in defining the forecast unit.

In order to avoid issues, we recommend setting the forecast unit explicitly, using the forecast_unit argument. This will simply drop unneeded columns, while making sure that all necessary, 'protected columns' like "predicted" or "observed" are retained.

Examples

as_forecast_binary(
  example_binary,
  predicted = "predicted",
  forecast_unit = c("model", "target_type", "target_end_date",
                    "horizon", "location")
)
#> ℹ Some rows containing NA values may be removed. This is fine if not
#>   unexpected.
#> Forecast type: binary
#> Forecast unit:
#> model, target_type, target_end_date, horizon, and location
#> 
#>       predicted observed                 model target_type target_end_date
#>           <num>   <fctr>                <char>      <char>          <Date>
#>    1:        NA     <NA>                  <NA>       Cases      2021-01-02
#>    2:        NA     <NA>                  <NA>      Deaths      2021-01-02
#>    3:        NA     <NA>                  <NA>       Cases      2021-01-09
#>    4:        NA     <NA>                  <NA>      Deaths      2021-01-09
#>    5:        NA     <NA>                  <NA>       Cases      2021-01-16
#>   ---                                                                     
#> 1027:     0.250        0 EuroCOVIDhub-baseline      Deaths      2021-07-24
#> 1028:     0.475        0       UMass-MechBayes      Deaths      2021-07-24
#> 1029:     0.450        0       UMass-MechBayes      Deaths      2021-07-24
#> 1030:     0.375        0  epiforecasts-EpiNow2      Deaths      2021-07-24
#> 1031:     0.300        0  epiforecasts-EpiNow2      Deaths      2021-07-24
#>       horizon location
#>         <num>   <char>
#>    1:      NA       DE
#>    2:      NA       DE
#>    3:      NA       DE
#>    4:      NA       DE
#>    5:      NA       DE
#>   ---                 
#> 1027:       2       IT
#> 1028:       3       IT
#> 1029:       2       IT
#> 1030:       3       IT
#> 1031:       2       IT

Create a `forecast` object for binary forecasts