example_truth_data_only

A data set with truth data for different quantities relevant in the
2020 UK Covid-19 epidemic, but no predictions

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

example_truth_data_only function

A data frame with 140 rows and 5 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>true_value</dt><dd>true observed values</dd></dl>

Format

Truth data only — example_truth_data_only

Truth data only

example_truth_data_only: Truth data only

Description

Usage

Arguments

Format