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PCA with spearman and kendall correlations
2 messages · BOURGADE Eric, David L Carlson
Spearman would be easier since you just convert the data to ranks and use the Pearson correlation:
set.seed(42) x <- data.frame(matrix(sample(1:9, 20, replace=TRUE), 10, 2)) x
X1 X2 1 9 5 2 9 7 3 3 9 4 8 3 5 6 5 6 5 9 7 7 9 8 2 2 9 6 5 10 7 6
cor(x)
X1 X2 X1 1.00000000 0.01897427 X2 0.01897427 1.00000000
cor(x, method="spearman")
X1 X2 X1 1.00000000 -0.03135181 X2 -0.03135181 1.00000000
cor(sapply(x, rank))
X1 X2 X1 1.00000000 -0.03135181 X2 -0.03135181 1.00000000 ---------------------------------------------- David L Carlson Associate Professor of Anthropology Texas A&M University College Station, TX 77843-4352
-----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r- project.org] On Behalf Of BOURGADE Eric Sent: Thursday, February 28, 2013 3:50 AM To: r-help at r-project.org Subject: [R] PCA with spearman and kendall correlations Hello, I would like to do a PCA with dudi.pca or PCA, but also with the use of Spearman or Kendall correlations Is it possible ? Otherwise, how can I do, according to you ? Thanking you in advance Eric Bourgade RTE France [[alternative HTML version deleted]]
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