Dear Donglei Hu, If you have two correlation coefficients, you may try cordif {multilevel} and cordif.dep {multilevel} for the independent correlations and for the dependent correlations, respectively. However, they are both based on the sampling distribution of correlation coeficient. A better approach may be the one based on the Fisher z transformation as suggested. The papers by Olkin and Finn (1995) and Steiger (1980) may be relevant for you.
If you have more than two independent correlation coefficients, meta-analysis may be a better choice. You may also choose between the approaches based on correlations (Hunter and Schmidt) or Fisher z transformation (Hedges and Olkin). If the correlations are dependent, structural equation modeling (SEM) is a more convenient approach (e.g., Cheung & Chan, 2004). Cheung, M.W.L., & Chan, W. (2004). Testing dependent correlation coefficients via structural equation modeling. Organizational Research Methods, 7, 206-223. Olkin, I., & Finn, J. D. (1995). Correlation redux. Psychological Bulletin, 118, 155-164. Steiger, J. H. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87, 245-251. Regards, Mike -- ----------------------------------------------------------- Mike W.L. Cheung Department of Psychology National University of Singapore Homepage: http://courses.nus.edu.sg/course/psycwlm/internet/ ----------------------------------------------------------- On 19 Sep 2006 01:22:47 +0200, Peter Dalgaard <[EMAIL PROTECTED]> wrote: > > "David Barron" <[EMAIL PROTECTED]> writes: > > > Is cor.test() in the stats packages what you mean? > > No, he wants to compare two correlation coefficients, not test that > one is zero. That's usually a misguided question, but if need be, the > Fisher z transform atanh(r) can be used to convert r to an > approximately normal variate with a known variance 1/(N-3) and > comparing r1 and r2 from two independent samples is straightforward. > The correlated case (like cor(x,y) vs cor(x,z)) is more complicated. > > > > > On 18/09/06, Hu, Donglei <[EMAIL PROTECTED]> wrote: > > > Hi, > > > > > > > > > > > > I calculated a few correlation coefficients. Now I want to know > whether > > > they are different from each other. Is there an R package that can do > > > such a comparison? Thanks for any suggestion. > > > > > > > > > > > > Best, > > > > > > Donglei Hu > > > > > > Department of Medicine > > > > > > UCSF > > > > > > > > > [[alternative HTML version deleted]] > > > > > > ______________________________________________ > > > 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. > > > > > > > > > -- > > ================================= > > David Barron > > Said Business School > > University of Oxford > > Park End Street > > Oxford OX1 1HP > > > > ______________________________________________ > > 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. > > > > -- > O__ ---- Peter Dalgaard Ă˜ster Farimagsgade 5, Entr.B > c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K > (*) \(*) -- University of Copenhagen Denmark Ph: (+45) > 35327918 > ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) > 35327907 > > ______________________________________________ > 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. > [[alternative HTML version deleted]]
______________________________________________ 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.