covsel

Variable selection for high-dimensionnal data with the COVSEL method (Roger et al. 2011).

datagen

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

covsel function

<dl>
<dt>X</dt>
<dd>X-data (\(n, p\)).</dd>
<dt>Y</dt>
<dd>Y-data (\(n, q\)).</dd>
<dt>nvar</dt>
<dd>Number of variables to select in <code>X</code>.</dd>
<dt>scaly</dt>
<dd>If <code>TRUE</code> (default), each column of \(Y\) is scaled by its standard deviation.</dd>
<dt>weights</dt>
<dd>Weights (\(n, 1\)) to apply to the training observations. Internally, weights are "normalized" to sum to 1. Default to <code>NULL</code> (weights are set to \(1 / n\)).</dd>
</dl>

Arguments

Variable selection for high-dimensionnal data with the COVSEL method (Roger et al. 2011).

CovSel — covsel

<dl>


<dt>X</dt>
<dd>X-data (\(n, p\)).</dd>


<dt>Y</dt>
<dd>Y-data (\(n, q\)).</dd>


<dt>nvar</dt>
<dd>Number of variables to select in <code>X</code>.</dd>


<dt>scaly</dt>
<dd>If <code>TRUE</code> (default), each column of \(Y\) is scaled by its standard deviation.</dd>


<dt>weights</dt>
<dd>Weights (\(n, 1\)) to apply to the training observations. Internally, weights are "normalized" to sum to 1. Default to <code>NULL</code> (weights are set to \(1 / n\)).</dd>


</dl>

covsel: CovSel

Description

Usage

Value

Arguments

References

Examples