rdcvNetParams

Custom parameters can be set in the function call or by manually setting "slots" in the resulting methParam object.

Predictive multivariate modelling for metabolomics.
Types: Classification and regression.
Methods: Partial Least Squares, Random Forest ans Elastic Net
Data structures: Paired and unpaired Validation: repeated double cross-validation (Westerhuis et al. (2008)<doi:10.1007/s11306-007-0099-6>, Filzmoser et al. (2009)<doi:10.1002/cem.1225>)
Variable selection: Performed internally, through tuning in the inner cross-validation loop.

Yingxiao Yan

MUVR2

Multivariate Methods with Unbiased Variable Selection

Carl Brunius

rdcvNetParams function

<dl><dt>robust</dt>
<dd>Robustness (slack) criterion for determining min and max knees (defaults to 0.05)</dd>
<dt>family</dt>
<dd>the options could be "gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"</dd>
<dt>nRepInner</dt>
<dd>how many nRepInner</dd>
<dt>NZV</dt>
<dd>NZV</dd>
<dt>oneHot</dt>
<dd>TRUE or FALSE using onehot endcoding or not</dd></dl>

Arguments

Make custom parameters for rdcvNet internal modelling — rdcvNetParams

<dl>

<dt>robust</dt>
<dd>Robustness (slack) criterion for determining min and max knees (defaults to 0.05)</dd>


<dt>family</dt>
<dd>the options could be "gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"</dd>


<dt>nRepInner</dt>
<dd>how many nRepInner</dd>


<dt>NZV</dt>
<dd>NZV</dd>


<dt>oneHot</dt>
<dd>TRUE or FALSE using onehot endcoding or not</dd>

</dl>

rdcvNetParams: Make custom parameters for rdcvNet internal modelling

Description

Usage

Value

Arguments

Examples