================================================================== 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. ================================================================== . posted to sci.stat.edu and e-mailed.
I haven't seen any answers attempted for this one - On 3 Jun 2004 06:16:36 -0700, [EMAIL PROTECTED] (Jo?l Rivest) wrote: > 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. Are you trying to extract the original model for prediction? or what? This is unfamiliar to me. It seems to me as if there would not be much point to it, unless the expected R-squared is very high, that is, the prediction is going to be very good. > > 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. > Are worried that the inverse-of-error is going to disrupt the statistical tests? Yes, but the only modeling I imagine will assume and require a good fit. So I would look at R-squared and sums of squares, not tests. What happens with no weighting? If weights are in a narrow range, that should be a good approximation to the model. If weights have a large range, then try the model when deleting the points that matter least - 10%, 20%, 50%. I'm curious about what application wants and ANOVA that uses predicted growth curves, especially if you do know the basis of the original predictions. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html
