assert_input_categorical

Function assesses whether the inputs correspond to the
requirements for scoring categorical, i.e. either nominal or ordinal
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_categorical function

<dl><dt>observed</dt>
<dd>Input to be checked. Should be a factor of length n with
N levels holding the observed values. n is the number of observations and
N is the number of possible outcomes the observed values can assume.</dd>
<dt>predicted</dt>
<dd>Input to be checked. Should be nxN matrix of predicted
probabilities, n (number of rows) being the number of data points and N
(number of columns) the number of possible outcomes the observed values
can assume.
If <code>observed</code> is just a single number, then predicted can just be a
vector of size N.
Values represent the probability that the corresponding value
in <code>observed</code> will be equal to the factor level referenced in
<code>predicted_label</code>.</dd>
<dt>predicted_label</dt>
<dd>Factor of length N with N levels, where N is the
number of possible outcomes the observed values can assume.</dd>
<dt>ordered</dt>
<dd>Value indicating whether factors have to be ordered or not.
Defaults to <code>NA</code>, which means that the check is not performed.</dd></dl>

Arguments

Assert that inputs are correct for categorical forecasts — assert_input_categorical

<dl>

<dt>observed</dt>
<dd>Input to be checked. Should be a factor of length n with
N levels holding the observed values. n is the number of observations and
N is the number of possible outcomes the observed values can assume.</dd>


<dt>predicted</dt>
<dd>Input to be checked. Should be nxN matrix of predicted
probabilities, n (number of rows) being the number of data points and N
(number of columns) the number of possible outcomes the observed values
can assume.
If <code>observed</code> is just a single number, then predicted can just be a
vector of size N.
Values represent the probability that the corresponding value
in <code>observed</code> will be equal to the factor level referenced in
<code>predicted_label</code>.</dd>


<dt>predicted_label</dt>
<dd>Factor of length N with N levels, where N is the
number of possible outcomes the observed values can assume.</dd>


<dt>ordered</dt>
<dd>Value indicating whether factors have to be ordered or not.
Defaults to <code>NA</code>, which means that the check is not performed.</dd>

</dl>

Assert that inputs are correct for categorical forecasts

assert_input_categorical: Assert that inputs are correct for categorical forecasts

Description

Usage

Value

Arguments