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Principal Components Analysis. A statistical method used to reduce the dimensionality of a dataset while keeping as much variance in the first principal components as possible. It can be used to visualise samples with many variables in 2-D or 3-D, thus allowing for a visual non-supervised grouping of points.

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How can I interpret the PCA results statistically for biological data? … I have used FactoMineR and factoextra libraries for PCA Scripts used: library(FactoMineR) res.PCA = PCA(df, scale.unit=TRUE, ncp=4, graph=F ) par(mfrow=c(1,2)) plot.PCA(res.PCA, axes=c(1, 2), choix= …
asked Jul 22 '18 by Dendrobium