mavg

Smoothing, by moving average, of the row observations (e.g. spectra) of a dataset.

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

mavg function

<dl>
<dt>X</dt>
<dd>X-data (\(n, p\)).</dd>
<dt>n</dt>
<dd>The number of points (i.e. columns of <code>X</code>) defining the window over wich is calculate each average. The smoothing is calculated for the point at the center of the window. Therefore, <code>n</code> must be an odd integer, and be higher or equal to 3.</dd>
</dl>

Arguments

Smoothing by moving average — mavg

<dl>


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


<dt>n</dt>
<dd>The number of points (i.e. columns of <code>X</code>) defining the window over wich is calculate each average. The smoothing is calculated for the point at the center of the window. Therefore, <code>n</code> must be an odd integer, and be higher or equal to 3.</dd>


</dl>

mavg: Smoothing by moving average

Description

Usage

Value

Arguments

Examples