ozone

Los Angeles ozone pollution data in 1976
(sources: Breiman &amp; Friedman 1985, Leisch &amp; Dimitriadou 2020).

datasets

Data exploration and prediction with focus on high dimensional data and chemometrics.
The package was initially designed about partial least squares regression and discrimination models and variants, in particular locally weighted PLS models (LWPLS). Then, it has been expanded to many other methods for analyzing high dimensional data.
The name 'rchemo' comes from the fact that the package is orientated to chemometrics, but most of the provided methods are fully generic to other domains.
Functions such as transform(), predict(), coef() and summary() are available. Tuning the predictive models is facilitated by generic functions gridscore() (validation dataset) and gridcv() (cross-validation). Faster versions are also available for models based on latent variables (LVs) (gridscorelv() and gridcvlv()) and ridge regularization (gridscorelb() and gridcvlb()).

Marion Brandolini-Bunlon

rchemo

Dimension Reduction, Regression and Discrimination for
Chemometrics

Benoit Jaillais

Jean-Michel Roger

Matthieu Lesnoff

ozone function

A list with 1 component: the matrix <code>X</code> with 366 observations, 13 variables. The variable to predict is V4.<dl>
 <dt><code>V1</code></dt>
<dd>Month: 1 = January, ..., 12 = December</dd> <dt><code>V2</code></dt>
<dd>Day of month</dd> <dt><code>V3</code></dt>
<dd>Day of week: 1 = Monday, ..., 7 = Sunday</dd> <dt><code>V4</code></dt>
<dd>Daily maximum one-hour-average ozone reading</dd> <dt><code>V5</code></dt>
<dd>500 millibar pressure height (m) measured at Vandenberg AFB</dd> <dt><code>V6</code></dt>
<dd>Wind speed (mph) at Los Angeles International Airport (LAX)</dd> <dt><code>V7</code></dt>
<dd>Humidity (%) at LAX</dd> <dt><code>V8</code></dt>
<dd>Temperature (degrees F) measured at Sandburg, CA</dd> <dt><code>V9</code></dt>
<dd>Temperature (degrees F) measured at El Monte, CA</dd> <dt><code>V10</code></dt>
<dd>Inversion base height (feet) at LAX</dd> <dt><code>V11</code></dt>
<dd>Pressure gradient (mm Hg) from LAX to Daggett, CA</dd> <dt><code>V12</code></dt>
<dd>Inversion base temperature (degrees F) at LAX</dd> <dt><code>V13</code></dt>
<dd>Visibility (miles) measured at LAX</dd> 
</dl>

Format

Los Angeles ozone pollution data in 1976
(sources: Breiman &amp; Friedman 1985, Leisch &amp; Dimitriadou 2020).

ozone — ozone

A list with 1 component: the matrix <code>X</code> with 366 observations, 13 variables. The variable to predict is V4.<dl>
 <dt><code>V1</code></dt>
<dd>Month: 1 = January, ..., 12 = December</dd>

 <dt><code>V2</code></dt>
<dd>Day of month</dd>

 <dt><code>V3</code></dt>
<dd>Day of week: 1 = Monday, ..., 7 = Sunday</dd>

 <dt><code>V4</code></dt>
<dd>Daily maximum one-hour-average ozone reading</dd>

 <dt><code>V5</code></dt>
<dd>500 millibar pressure height (m) measured at Vandenberg AFB</dd>

 <dt><code>V6</code></dt>
<dd>Wind speed (mph) at Los Angeles International Airport (LAX)</dd>

 <dt><code>V7</code></dt>
<dd>Humidity (%) at LAX</dd>

 <dt><code>V8</code></dt>
<dd>Temperature (degrees F) measured at Sandburg, CA</dd>

 <dt><code>V9</code></dt>
<dd>Temperature (degrees F) measured at El Monte, CA</dd>

 <dt><code>V10</code></dt>
<dd>Inversion base height (feet) at LAX</dd>

 <dt><code>V11</code></dt>
<dd>Pressure gradient (mm Hg) from LAX to Daggett, CA</dd>

 <dt><code>V12</code></dt>
<dd>Inversion base temperature (degrees F) at LAX</dd>

 <dt><code>V13</code></dt>
<dd>Visibility (miles) measured at LAX</dd>

 
</dl>

ozone: ozone

Description

Usage

Arguments

Format

Examples