I wrote >>> I am puzzled by the performance of LME in situations where there are >>> missing data. As I >>> understand it, one of the strengths of this sort of model is how well it >>> deals with missing >>> data, yet lme requires nonmissing data. >
Mark Difford replied >You are confusing missing data with an unbalanced design. A strengths of LME >is that it copes with the latter, which aov() does not. > Thanks for your reply, but I don't believe I am confused with respect to missing data in mixed models. See e.g. Hedeker and Gibbons, Longitudinal Data Analysis, which repeatedly stresses that mixed models provide good estimates if the data are missing at random. Regards Peter Peter L. Flom, PhD Statistical Consultant Website: www DOT peterflomconsulting DOT com Writing; http://www.associatedcontent.com/user/582880/peter_flom.html Twitter: @peterflom ______________________________________________ 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.
