bordeauxNA

Quality of Bordeaux wines (<code>Quality</code>) and four potentially predictive
variables (<code>Temperature</code>, <code>Sunshine</code>, <code>Heat</code> and
<code>Rain</code>).

datasets

Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <arXiv:1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

Frederic Bertrand

plsRglm

Partial Least Squares Regression for Generalized Linear Models

Myriam Maumy-Bertrand

bordeauxNA function

A data frame with 34 observations on the following 5 variables.<dl>
<dt>Temperature</dt>
<dd>a numeric vector</dd><dt>Sunshine</dt>
<dd>a numeric vector</dd>
 <dt>Heat</dt>
<dd>a numeric
vector</dd>
 <dt>Rain</dt>
<dd>a numeric vector</dd>
 <dt>Quality</dt>
<dd>an
ordered factor with levels <code>1</code> &lt; <code>2</code> &lt; <code>3</code></dd>
 
</dl>

Format

Quality of wine dataset — bordeauxNA

A data frame with 34 observations on the following 5 variables.<dl>
<dt>Temperature</dt>
<dd>a numeric vector</dd>

<dt>Sunshine</dt>
<dd>a numeric vector</dd>
 <dt>Heat</dt>
<dd>a numeric
vector</dd>
 <dt>Rain</dt>
<dd>a numeric vector</dd>
 <dt>Quality</dt>
<dd>an
ordered factor with levels <code>1</code> &lt; <code>2</code> &lt; <code>3</code></dd>
 
</dl>

bordeauxNA: Quality of wine dataset

Description

Arguments

Format

Details

References

Examples