continuous_example_data

A data set with continuous predictions in a sample-based format relevant in the
2020 UK Covid-19 epidemic.

datasets

Combines a collection of metrics and proper scoring rules
(Tilmann Gneiting & Adrian E Raftery (2007)
<doi:10.1198/016214506000001437>) with an easy to
use wrapper that can be used to automatically evaluate predictions.
Apart from proper scoring rules functions are provided to assess bias,
sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski,
Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019)
<doi:10.1371/journal.pcbi.1006785>) of forecasts.
Several types of predictions can be evaluated:
probabilistic forecasts (generally
predictive samples generated by Markov Chain Monte Carlo procedures),
quantile forecasts or point forecasts. Observed values and predictions
can be either continuous, integer, or binary. Users can either choose
to apply these rules separately in a vector / matrix format that can
be flexibly used within other packages, or they can choose to do an
automatic evaluation of their forecasts. This is implemented with
'data.table' and provides a consistent and very efficient framework for
evaluating various types of predictions.

Nikos Bosse

scoringutils

Utilities for Scoring and Assessing Predictions

Sam Abbott 

Johannes Bracher 

Joel Hellewell

Sophie Meakins 

James Munday

Katharine Sherratt

Sebastian Funk

continuous_example_data function

A data frame with 13,429 rows and 10 columns:<dl class="dl-horizontal">
 <dt>value_date</dt><dd>the date for which a prediction was made</dd>
 <dt>value_type</dt><dd>the target to be predicted (short form)</dd>
 <dt>geography</dt><dd>the region for which a prediction was made</dd>
 <dt>value_desc</dt><dd>long form description of the prediction target</dd>
 <dt>model</dt><dd>name of the model that generated the forecasts</dd>
 <dt>creation_date</dt><dd>date on which the forecast was made</dd>
 <dt>horizon</dt><dd>forecast horizon in days</dd>
 <dt>prediction</dt><dd>prediction value for the corresponding sample</dd>
 <dt>sample</dt><dd>id for the corresponding sample</dd>
 <dt>true_value</dt><dd>true observed values</dd>
</dl>

Format

Continuous Forecast Example Data — continuous_example_data

A data frame with 13,429 rows and 10 columns:<dl class='dl-horizontal'>
 <dt>value_date</dt><dd>the date for which a prediction was made</dd>
 <dt>value_type</dt><dd>the target to be predicted (short form)</dd>
 <dt>geography</dt><dd>the region for which a prediction was made</dd>
 <dt>value_desc</dt><dd>long form description of the prediction target</dd>
 <dt>model</dt><dd>name of the model that generated the forecasts</dd>
 <dt>creation_date</dt><dd>date on which the forecast was made</dd>
 <dt>horizon</dt><dd>forecast horizon in days</dd>
 <dt>prediction</dt><dd>prediction value for the corresponding sample</dd>
 <dt>sample</dt><dd>id for the corresponding sample</dd>
 <dt>true_value</dt><dd>true observed values</dd>
</dl>

Continuous Forecast Example Data

continuous_example_data: Continuous Forecast Example Data

Description

Usage

Arguments

Format