I am fitting individual growth models using nlme (multilevel models with repeated measurements nested within the individual), and I am trying to calculate the Pseudo R-squared for the models (an overall summary of the total outcome variability explained). Singer and Willett (2003) recommend calculating Pseudo R-squared in multilevel modeling by squaring the sample correlation between observed and predicted values (across the sample for each person on each occasion of measurement).
My question is which set of predicted values should I use from nlme in that calculation? From my models in nlme, I receive two sets of fitted values. Reading the description of the fitted lme values (http://stat.ethz.ch/R-manual/R-patched/library/nlme/html/fitted.lme.html), there appear to be two sets of fitted values that correspond to levels of grouping, where the first set of fitted values (Level 0) correspond to the population fitted values and it moves to more innermore groupings as the levels increase (e.g., I suppose Level 1 corresponds to the individual-level fitted values in my data). I'm not sure I understand the distinction between population fitted values and individual-level fitted values because each individual and each measurement occasion has an estimate for both (population and individual fitted estimates). Could you please explain the distinction and which one I should be using to calculate the Pseudo R-squared as suggested by Singer and Willett (2003)? Thanks so much for your help! -- View this message in context: http://r.789695.n4.nabble.com/Calculating-Pseudo-R-squared-from-nlme-tp4413825p4413825.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
