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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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You can easily color 3D pca plots in R based on the code given below: library("scatterplot3d") colors <- c("#999999", "#E69F00", "#56B4E9") # Number of color according to the number of groups colors < … Edited Response Using the data given below I plotted a 3D pca that may help solve your problem, "Col1" "Col2" "Col3" "Col4" "colend" "H1" 5.1 3.5 1.4 0.2 "HSC" "H2" 4.9 3 1.4 0.2 "HSC" "H3" 4.7 3.2 1.3 …
answered Oct 30 '18 by Ammar Sabir Cheema