weighted_cor

Calculates weighted Pearson correlation coefficient between actual and predicted
values by the help of <code><a href="/link/stats%3A%3Acov.wt()?package=MetricsWeighted&version=1.0.4" data-mini-rdoc="MetricsWeighted::stats::cov.wt()">stats::cov.wt()</a></code>.

Provides weighted versions of several metrics and performance
measures used in machine learning, including average unit deviances of
the Bernoulli, Tweedie, Poisson, and Gamma distributions, see
Jorgensen B. (1997, ISBN: 978-0412997112).  The package also contains
a weighted version of generalized R-squared, see e.g. Cohen, J. et al.
(2002, ISBN: 978-0805822236).  Furthermore, 'dplyr' chains are
supported.

Michael Mayer

MetricsWeighted

Weighted Metrics and Performance Measures for Machine Learning

Christian Lorentzen

weighted_cor function

<dl><dt>actual</dt>
<dd>Observed values.</dd>
<dt>predicted</dt>
<dd>Predicted values.</dd>
<dt>w</dt>
<dd>Optional case weights.</dd>
<dt>na.rm</dt>
<dd>Should observations with missing values in <code>actual</code> or <code>predicted</code>
be removed? Default is <code>FALSE</code>.</dd>
<dt>...</dt>
<dd>Further arguments passed to <code><a href="/link/stats%3A%3Acov.wt()?package=MetricsWeighted&version=1.0.4" data-mini-rdoc="MetricsWeighted::stats::cov.wt()">stats::cov.wt()</a></code>.</dd></dl>

Arguments

Calculates weighted Pearson correlation coefficient between actual and predicted
values by the help of <code><a href='https://rdrr.io/r/stats/cov.wt.html'>stats::cov.wt()</a></code>.

Weighted Pearson Correlation — weighted_cor

<dl>

<dt>actual</dt>
<dd>Observed values.</dd>


<dt>predicted</dt>
<dd>Predicted values.</dd>


<dt>w</dt>
<dd>Optional case weights.</dd>


<dt>na.rm</dt>
<dd>Should observations with missing values in <code>actual</code> or <code>predicted</code>
be removed? Default is <code>FALSE</code>.</dd>


<dt>...</dt>
<dd>Further arguments passed to <code><a href='https://rdrr.io/r/stats/cov.wt.html'>stats::cov.wt()</a></code>.</dd>

</dl>

weighted_cor: Weighted Pearson Correlation

Description

Usage

Value

Arguments

Examples