Hello- I'm trying to do some repeated measures ANOVAs. In the past, using SAS, I have used the framework outlined in Littell et al.'s "SAS System for Mixed Models", using the REPEATED statement in PROC MIXED to model variation across time within an experimental unit. SAS allows you to specify different within-unit covariance structures (e.g., compound symmetric, AR(1), etc.) to determine the best model.
I'm having trouble figuring out how to do a similar analysis in R. While 'lme' will let you choose the class of correlation structure to use, it seems to require that you specify this structure rather than using the data to estimate the covariance matrix. For example, it seems that to specify 'corAR1' as the correlation structure, you have to pick a value for rho, the autoregressive parameter. So, my question: is there a way to tell 'lme' what sort of covariance structure you'd like to model, and then let the function estimate the covariances? Or, alternatively, is there a better way to go about this sort of repeated measures analysis in R? I've exhausted my available R resources and done a pretty good search of the help archives without finding a clear answer. Thanks much! Chris ******* Chris Solomon Center for Limnology Univ. of Wisconsin Phone: (608) 263-2465 ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html