Hi list I am just beginning to understand the complexities of linear mixed effects models. Maybe someone can give advise concerning the following problem:
I have two groups of surgical patients in which repeated laboratory measurements were taken over time after surgery. I decided that lme would be the best model to fit the data. I already fitted the model lme(logratio ~ gr*I(pod-10) + I(pod^2-10) + I(pod^3-10), data=xyz, random = ~ pod|subj) where gr = two groups; pod = postoperative day; subj = patient; logratio = log of value at day pod/preoperative value: log(post/pre) but these questions remain: 1. Is lme the best model to fit the data? Other suggestions? 2. Since the ratio had no gaussian distribution I took the log which seems to have a normal distribution. Is this OK? 3. I shifted the intercept to pod 10 because at this point the difference of the intercept is significant different whereas the difference at 0 is not significant. Can I do this? 4. Inspection of the data showed that a polynomial regression would be a better fit for the data. I tried several polynomial regressions up to pod^5. The above model had the lowest AIC, BIC and logLik. When I use Anova to compare the models there I get the warning message: "Fitted objects with different fixed effects. REML comparisons are not meaningful." What can I use instead to compare the models? 5. For random I used only pod and not pod^x. Is this correct? 6. Omitting the group factor from pod^2 and pod^3 the model had a slightly better fit. Can I do this? 7. Can I assume that the data is heteroskedastic? How do I apply the 'weights' in the above model? I am sorry if some questions may sound weird but I am just beginning to understand this (for me) rather complex concept. Thanks for any help. -- Armin Goralczyk, M.D. Dept. of General Surgery University of Göttingen Göttingen, Germany http://www.chirurgie-goettingen.de ______________________________________________ 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.
