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
darteta001 at ikasle.ehu.es 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 >It's probably not massively wrong, given that the interpretation of correlation coefficients is usually not very clear anyway (excepting maybe nearly-perfect correlated cases). However, a Spearman correlation does have the rather nice feature of being independent of the values you assign to M2, and of any monotone transformation of M1 too. -- O__ ---- Peter Dalgaard ?ster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907
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, <darteta001@ikasle.ehu.es> 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]]