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 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help