Looking over the code below, I think this patched version might return a better answer:
spec.cor <- function(dat, r, ...) { x <- cor(dat, ...) x[upper.tri(x, TRUE)] <- NA i <- which(abs(x) >= r, arr.ind = TRUE) data.frame(V1 = rownames(x)[i[,1]], V2 = colnames(x)[i[,2]], Value = x[i]) } On Thu, Nov 24, 2011 at 12:03 AM, R. Michael Weylandt <michael.weyla...@gmail.com> wrote: > There have been two threads dealing with this in the last few weeks: > please search the recent archives for those threads for a good > discussion -- end result: Josh Wiley provided a useful little function > to do so that I'll copy below. RSeek.org is a good place to do your > searching. > > spec.cor <- function(dat, r, ...) { > x <- cor(dat, ...) > x[upper.tri(x, TRUE)] <- NA > i <- which(abs(x) >= r, arr.ind = TRUE) > data.frame(matrix(colnames(x)[as.vector(i)], ncol = 2), value = x[i]) > } > > Michael > > On Wed, Nov 23, 2011 at 7:34 AM, mgranlie <m...@granlie.dk> wrote: >> Hello. >> >> I have a large dataset with sales pr month for 56 products with 10 months >> and i have tried to see how the sales are correlated using >> cor() >> >> This has given me a 56X56 matrix with the R value for each product pair. >> Most of these correlations are insignificant, and i want only to retain the >> instances were the R value is significant (for 10 observations it should be >> above 0.64) >> >> Can someone help with this? >> >> -- >> View this message in context: >> http://r.789695.n4.nabble.com/Correlation-matrix-removing-insignificant-R-values-tp4099412p4099412.html >> Sent from the R help mailing list archive at Nabble.com. >> >> ______________________________________________ >> R-help@r-project.org 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. ______________________________________________ R-help@r-project.org 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.