It is easy to worry too much about using numbers to represent order when using statistics like the correlation. this little example shows that the correlation is essentially a rank-order correlation itself: > x <- 1:20 > y <- x^2 > cor(x,y) [1] 0.9713482
x and y are definitely not linearly related, yet the correlation is extremely high. As Peter suggests, you could be 'safe' using a Spearman correlation, which is identical to cor(rank(x), rank(y)). But the rank transform may be more destructive to your data than need be. gary mcclelland colorado On Fri, Feb 8, 2008 at 9:14 AM, <[EMAIL PROTECTED]> wrote: > Dear list > > I would like to compare two measurements of disease severity (M1 and > M2), one of the is continuous (M1 ranging from 1 to 10) and the other > is ordinal (M2 takes Low, Medium, high and very high). Do you think is > ok to use cor() function to test whether the two agree, i.e correlate? > I am afraid that if I set M2 to 1,2,3 and 4, the function cor() will > take them as continuous and therefore lose intrepretation. > > Thanks for your commments > > David > > ______________________________________________ > 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. > [[alternative HTML version deleted]] ______________________________________________ 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.
