assert_input_quantile

Function assesses whether the inputs correspond to the
requirements for scoring quantile-based forecasts.

internal_input_check

Facilitate the evaluation of forecasts in a convenient
framework based on data.table. It allows user to to check their forecasts
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 mostly focuses on
the evaluation of probabilistic forecasts and allows evaluating several
different forecast types and input formats. Find more information about the
package in the Vignettes as well as in the accompanying paper,
<doi:10.48550/arXiv.2205.07090>.

Nikos Bosse

scoringutils

Utilities for Scoring and Assessing Predictions

Sam Abbott

Hugo Gruson

Johannes Bracher

Toshiaki Asakura 

James Mba Azam

Sebastian Funk

Michael Chirico

assert_input_quantile function

<dl><dt>observed</dt>
<dd>Input to be checked. Should be a numeric vector with the
observed values of size n.</dd>
<dt>predicted</dt>
<dd>Input to be checked. Should be nxN matrix of predictive
quantiles, n (number of rows) being the number of data points and N
(number of columns) the number of quantiles per forecast.
If <code>observed</code> is just a single number, then predicted can just be a
vector of size N.</dd>
<dt>quantile_level</dt>
<dd>Input to be checked. Should be a vector of size N that
denotes the quantile levels corresponding to the columns of the prediction
matrix.</dd>
<dt>unique_quantile_levels</dt>
<dd>Whether the quantile levels are required to be
unique (<code>TRUE</code>, the default) or not (<code>FALSE</code>).</dd></dl>

Arguments

Assert that inputs are correct for quantile-based forecast — assert_input_quantile

<dl>

<dt>observed</dt>
<dd>Input to be checked. Should be a numeric vector with the
observed values of size n.</dd>


<dt>predicted</dt>
<dd>Input to be checked. Should be nxN matrix of predictive
quantiles, n (number of rows) being the number of data points and N
(number of columns) the number of quantiles per forecast.
If <code>observed</code> is just a single number, then predicted can just be a
vector of size N.</dd>


<dt>quantile_level</dt>
<dd>Input to be checked. Should be a vector of size N that
denotes the quantile levels corresponding to the columns of the prediction
matrix.</dd>


<dt>unique_quantile_levels</dt>
<dd>Whether the quantile levels are required to be
unique (<code>TRUE</code>, the default) or not (<code>FALSE</code>).</dd>

</dl>

Assert that inputs are correct for quantile-based forecast

assert_input_quantile: Assert that inputs are correct for quantile-based forecast

Description

Usage

Value

Arguments