Test significance of clusters on a PCA plot
pca, r, statistics
Solution
You could use a PERMANOVA to partition the euclidean distance by your groups:
data(iris)
require(vegan)
# PCA
iris_c <- scale(iris[ ,1:4])
pca <- rda(iris_c)
# plot
plot(pca, type = 'n', display = 'sites')
cols <- c('red', 'blue', 'green')
points(pca, display='sites', col = cols[iris$Species], pch = 16)
ordihull(pca, groups=iris$Species)
ordispider(pca, groups = iris$Species, label = TRUE)
# PerMANOVA - partitioning the euclidean distance matrix by species
adonis(iris_c ~ Species, data = iris, method='eu')
Problem
Is it possible to test the significance of clustering between 2 known groups on a PCA plot? To test how close they are or the amount of spread (variance) and the amount of overlap between clusters etc.