visualizePCA: Generate PCA plots with optimized clustering
In audrina/pcclust: PCA Analysis and Visualization for Clustering

Description Usage Arguments Value See Also Examples

Baseline PCA visualizations including standard 2-component PCA plot, density plot, and variance contribution pie chart.

1	visualizePCA(bestPCSet, clusters, pcObj, outDir = ".")

`bestPCSet`	a matrix whose columns contain the optimal principal components.
`clusters`	vector of predicted labels
`pcObj`	a PCA object
`outDir`	path to ouput directory where pcclust_visualization folder will be generated. Defaults to current working directory.

path to pcclust_visualization output directory. Outputs high quality .svg files for each of the 3 plots.

ggplot

svg

data <- validateAndLoadData(iris)
pcObj <- prcomp(data)
pcData <- pcObj$x
iterationResults <- executePCFiltering(pcData)
bestPCSet <- iterationResults[[length(iterationResults)]]
clusterResults <- evaluateClusterQuality(bestPCSet)
optimalModel <- determineOptimalModel(bestPCSet)
clusters <- optimalModel$classification
out <- visualizePCA(bestPCSet, clusters, pcObj)