================================================================== The gateway between this list and the sci.stat.edu newsgroup will be disabled on June 9. This list will be discontinued on June 21. Subscribe to the new list EDSTAT-L at Penn State using the web interface at http://lists.psu.edu/archives/edstat-l.html. ================================================================== . Hi,
I am looking for references or suggestions on how to analyse predicted values (e.g. as if they were observed ones) in a mixed model. I have individual growth curves providing predicted values of weight for a given age and I would like to analyse those predicted values in a mixed model, considering their prediction errors. Those prediction errors are specific to each individual. Some methods such as random regressions and repeated measures analysis with different covariance struture were considered, but didn't worked properly because either of the data structure or the complexity of the mixed model (I am using SAS - I know there are packages that would be OK for the job, probably ASREML). A suggestion is to use weighted analysis, but it seems that the weighting variable is more complicated to calculate than just taking the inverse of the prediction error. Thanks, Jo�l Rivest Apology for my english writing.
