checkinput

This can be run to test if the command input of parameters contradict each 
other and check the structure of the data. If something goes wrong, warning 
messages are given.

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

checkinput function

<dl><dt>X</dt>
<dd>The original data of X, not the result after onehotencoding</dd>
<dt>Y</dt>
<dd>The original data of Y</dd>
<dt>ML</dt>
<dd>ML in MUVR2</dd>
<dt>DA</dt>
<dd>DA in MUVR2</dd>
<dt>method</dt>
<dd>RF or PLS so far in MUVR2</dd>
<dt>fitness</dt>
<dd>fitness in MUVR2</dd>
<dt>nInner</dt>
<dd>nInnerin MUVR2</dd>
<dt>nOuter</dt>
<dd>nOuter in MUVR2</dd>
<dt>varRatio</dt>
<dd>varRatio in MUVR2</dd>
<dt>scale</dt>
<dd>scale</dd>
<dt>modReturn</dt>
<dd>modReturn in MUVR2</dd>
<dt>logg</dt>
<dd>logg in MUVR2</dd>
<dt>parallel</dt>
<dd>parallel in MUVR2</dd></dl>

Arguments

Check input — checkinput

<dl>

<dt>X</dt>
<dd>The original data of X, not the result after onehotencoding</dd>


<dt>Y</dt>
<dd>The original data of Y</dd>


<dt>ML</dt>
<dd>ML in MUVR2</dd>


<dt>DA</dt>
<dd>DA in MUVR2</dd>


<dt>method</dt>
<dd>RF or PLS so far in MUVR2</dd>


<dt>fitness</dt>
<dd>fitness in MUVR2</dd>


<dt>nInner</dt>
<dd>nInnerin MUVR2</dd>


<dt>nOuter</dt>
<dd>nOuter in MUVR2</dd>


<dt>varRatio</dt>
<dd>varRatio in MUVR2</dd>


<dt>scale</dt>
<dd>scale</dd>


<dt>modReturn</dt>
<dd>modReturn in MUVR2</dd>


<dt>logg</dt>
<dd>logg in MUVR2</dd>


<dt>parallel</dt>
<dd>parallel in MUVR2</dd>

</dl>

checkinput: Check input

Description

Usage

Value

Arguments

Examples