plotPCA

Customised PCA score plots with the possibility to choose PCs, exporting to png and the possibility to add color or different plotting symbols according to variable.

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

plotPCA function

<dl><dt>pca</dt>
<dd>A `prcomp` object</dd>
<dt>PC1</dt>
<dd>Principal component on x-axis</dd>
<dt>PC2</dt>
<dd>Principal component on y-axis</dd>
<dt>file</dt>
<dd>If specified provides the name of a png export file. Otherwise normal plot.</dd>
<dt>colVar</dt>
<dd>Continuous variable for coloring observations (40 cuts)</dd>
<dt>symbVar</dt>
<dd>Categorical/discrete variable for multiple plot symbols</dd>
<dt>main</dt>
<dd>If provided provides a main title of the plot</dd></dl>

Arguments

PCA score plot — plotPCA

<dl>

<dt>pca</dt>
<dd>A `prcomp` object</dd>


<dt>PC1</dt>
<dd>Principal component on x-axis</dd>


<dt>PC2</dt>
<dd>Principal component on y-axis</dd>


<dt>file</dt>
<dd>If specified provides the name of a png export file. Otherwise normal plot.</dd>


<dt>colVar</dt>
<dd>Continuous variable for coloring observations (40 cuts)</dd>


<dt>symbVar</dt>
<dd>Categorical/discrete variable for multiple plot symbols</dd>


<dt>main</dt>
<dd>If provided provides a main title of the plot</dd>

</dl>

plotPCA: PCA score plot

Description

Usage

Value

Arguments

Examples