Thursday, May 20, 2010

Tutorial: Principal Components Analysis (PCA) in R

Found this tutorial by Emily Mankin on how to do principal components analysis (PCA) using R. Has a nice example with R code and several good references. The example starts by doing the PCA manually, then uses R's built in prcomp() function to do the same PCA.

Principle Components Analysis: A How-To Manual for R

10 comments:

  1. Hi there,
    great posting but unfortunately I'm having some difficulties executing the following code from Box 3:

    X=Xoriginal-matrix(rep(rm, dim(X)[2]), nrow=dim(X)[1])

    Shouldn't the X on the RHS of the formula be Xoriginal?
    Thanks,
    Ruben

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  2. I've personally had memory allocation issues when doing PCA with genetic data (e.g., implementing my own Eigenstrat-type method). Has anyone tried this version with, say, 100k markers & 1000 samples or something comparable?

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  3. is the manual shown above available..?I have been trying for a couple of days to d/l it
    Thanks
    Sajan

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  4. Hmm, I'm having trouble accessing it too. I'll email the folks at Colorado and see if they can get this back online.

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  5. Can't read the article!

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  6. You can find it here: http://www.scribd.com/doc/39718072/PCA-How-To-1

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  7. Great article ! Thank you

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  8. Very approximative maths, plenty of typos (e.g., if X is an mxn matrix, XX^t is certainly not a nxn matrix.
    NOT recommended.

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  9. I have problem reading/downloading the article

    ReplyDelete

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Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License.