Create a forecast object for nominal forecasts
Source: R/class-forecast-nominal.R
as_forecast_nominal.RdProcess 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_nominal(
data,
forecast_unit = NULL,
observed = NULL,
predicted = NULL,
predicted_label = 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. Seeget_forecast_unit()for details. IfNULL(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
datathat contains the observed values. This column will be renamed to "observed".- predicted
(optional) Name of the column in
datathat contains the predicted values. This column will be renamed to "predicted".- predicted_label
(optional) Name of the column in
datathat denotes the outcome to which a predicted probability corresponds to. This column will be renamed to "predicted_label".
Details
Nominal forecasts are a form of categorical forecasts and represent a generalisation of binary forecasts to multiple outcomes. The possible outcomes that the observed values can assume are not ordered.
Required input
The input needs to be a data.frame or similar with the following columns:
observed: Column with observed values of typefactorwith N levels, where N is the number of possible outcomes. The levels of the factor represent the possible outcomes that the observed values can assume.predicted:numericcolumn with predicted probabilities. The values represent the probability that the observed value is equal to the factor level denoted inpredicted_label. Note that forecasts must be complete, i.e. there must be a probability assigned to every possible outcome and those probabilities must sum to one.predicted_label:factorwith N levels, denoting the outcome that the probabilities inpredictedcorrespond to.
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_nominal 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.
See also
Other functions to create forecast objects:
as_forecast_binary(),
as_forecast_ordinal(),
as_forecast_point(),
as_forecast_quantile(),
as_forecast_sample()
Examples
as_forecast_nominal(
na.omit(example_nominal),
predicted = "predicted",
forecast_unit = c("model", "target_type", "target_end_date",
"horizon", "location")
)
#> Forecast type: nominal
#> Forecast unit:
#> model, target_type, target_end_date, horizon, and location
#>
#> observed predicted_label predicted model target_type
#> <fctr> <fctr> <num> <char> <char>
#> 1: low low 0.525 EuroCOVIDhub-ensemble Cases
#> 2: low low 0.075 EuroCOVIDhub-baseline Cases
#> 3: low low 0.150 epiforecasts-EpiNow2 Cases
#> 4: medium low 0.100 EuroCOVIDhub-ensemble Deaths
#> 5: medium low 0.275 EuroCOVIDhub-baseline Deaths
#> ---
#> 2657: low medium 0.300 EuroCOVIDhub-baseline Deaths
#> 2658: medium medium 0.850 UMass-MechBayes Deaths
#> 2659: low medium 0.825 UMass-MechBayes Deaths
#> 2660: medium medium 0.275 epiforecasts-EpiNow2 Deaths
#> 2661: low medium 0.375 epiforecasts-EpiNow2 Deaths
#> target_end_date horizon location
#> <Date> <num> <char>
#> 1: 2021-05-08 1 DE
#> 2: 2021-05-08 1 DE
#> 3: 2021-05-08 1 DE
#> 4: 2021-05-08 1 DE
#> 5: 2021-05-08 1 DE
#> ---
#> 2657: 2021-07-24 2 IT
#> 2658: 2021-07-24 3 IT
#> 2659: 2021-07-24 2 IT
#> 2660: 2021-07-24 3 IT
#> 2661: 2021-07-24 2 IT