Dear R users,
Does anyone know why the following two ways to calculate
correlation variance give different answers? I also obtain
different answers when I use, say, "spearman" method in
cor(). The problem does not happen in R 1.7.1 ("pearson"
correlation only, of course in R 1.7.1).
> set.seed(1234)
> x <- matrix(rnorm(10*5),10,5)
> y1 <- cor(x)
> y2 <- cor(x, use="pair")
> y1;y2
[,1] [,2] [,3] [,4]
[,5]
[1,] 1.0000000 -0.17528322 -0.5528785 -0.33876389
-0.49755947
[2,] -0.1752832 1.00000000 -0.2776360 -0.04840035
0.05265522
[3,] -0.5528785 -0.27763602 1.0000000 0.16272829
0.38392034
[4,] -0.3387639 -0.04840035 0.1627283 1.00000000
0.85404798
[5,] -0.4975595 0.05265522 0.3839203 0.85404798
1.00000000
[,1] [,2] [,3] [,4]
[,5]
[1,] 1.0000000 -0.17348156 -0.5523156 -0.33585411
-0.48292994
[2,] -0.1734816 0.99965819 -0.2743654 -0.04417098
0.05661364
[3,] -0.5523156 -0.27436539 0.9990913 0.16439438
0.38457068
[4,] -0.3358541 -0.04417098 0.1643944 0.99862845
0.85389126
[5,] -0.4829299 0.05661364 0.3845707 0.85389126
0.99985356
Thanks,
Ming-Chung Li