pcaPlot: Title Using a PCA data projection approach to display your ML...

In juanbot/G2PML:

Description

This method can be useful to see how your disease genes and your predicction genes are scattered through a PCA dimensions data projection.

Usage

pcaPlot(fsdata, r = 0.6, ensemble, bestPCAs = F)

Arguments

fsdata	The results from caret::featureSelection
r	It has the same meaning as in caret::featureSelection. Higher the value, most selective is the filter to select which features appear in the plot.
ensemble	The result to call caret::ensembleLearnKFold
bestPCAs	If TRUE, we select PCA1 and 2 on the basis of those with best p-value on the correlation with phenotype

Value

Just a plot, nothing to return

Examples

pcaPlot(fsdata=fspd,ensemble=pdmodel,r=0.4,bestPCAs=T)

juanbot/G2PML documentation built on Aug. 1, 2020, 5:07 a.m.