Hello, I'm using a very large data set (n > 100,000 for 7 columns), for which I'm pretty happy dealing with pairwise-deleted correlations to populate my correlation table. E.g.,
a <- cor(cbind(col1, col2, col3),use="pairwise.complete.obs") ...however, I am interested in the number of cases used to compute each cell of the correlation table. I am unable to find such a function via google searches, so I wrote one of my own. This turns out to be highly inefficient (e.g., it takes much, MUCH longer than the correlations do). Any hints, regarding other functions to use or ways to maket his speedier, would be much appreciated! pairwise.n <- function(df=stop("Must provide data frame!")) { if (!is.data.frame(df)) { df <- as.data.frame(df) } colNum <- ncol(df) result <- matrix(data=NA,nrow=colNum,ncol=ncolNum,dimnames=list(colnames(df),colnames(df))) for(i in 1:colNum) { for (j in i:colNum) { result[i,j] <- length(df[!is.na(df[i])&!is.na(df[j])])/colNum } } result } -- Adam D. I. Kramer University of Oregon ______________________________________________ R-help@stat.math.ethz.ch mailing list 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.