I'm trying to find an efficient way to find the N size on correlations produced when using the pairwise option in cor().
Here is a sample to illustrate: ### Create a sample data frame tmp <- data.frame(v1 = rnorm(10), v2 = rnorm(10), v3 = rnorm(10), v4 = rnorm(10)) ### Create some random missingness for(i in 1:4) tmp[sample(1:10, 2, replace = FALSE), i] <- NA ### Correlate cor(tmp, use = 'pairwise') Now, a REALLY bad idea would be this (but conceptually it illustrates what I want) ### Identify all column pairs pairs <- combn(4,2) ### Now, write code to loop over each pair of columns and identify where both rows are TRUE !is.na(tmp[, pairs[,1]]) Of course doing this when the number of pairwise combinations is silly. So, hmmm, I don't see as a by-product of the cor() function N sizes, and certainly looping over pairs of columns would be doable, but not efficient, but any suggestions on this? Thanks, Harold ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
