John, your question is confusing. After reading it twice, I still cannot figure out what exactly you want to compare.
Your model "a" is the unrestricted model, and model "b" is a restricted version of model "a" (i.e., b is a hiearchically reduced version of a, or put differently, all coefficients of b are in a with a having additional coefficients). Thus, it is appropriate to compare the models (also called nested models). Comparing c with a and d with a is also appropriate for the same reason. However, note that depedent on discipline, it may be highly unconventional to fit an interaction without all direct effects of the interacted variables (the reason for this being that you may get biased estimates). What you might consider is: 1. Run an intercept only model 2. Run a model with group and time 3. Run a model with group, time, and the interaction Then compare 2 to 1, and 3 to 2. This tells you whether including more variables (hierarchically) makes your model better. HTH, Daniel On a different note, if lme fits with "restricted maximum likelihood," I think I remember that you cannot compare them. You have to fit them with "maximum likelihood." I am pointing this out because lmer with restricted maximum likelihood by standard, so lme might too. ------------------------- cuncta stricte discussurus ------------------------- -----Ursprüngliche Nachricht----- Von: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Im Auftrag von John Sorkin Gesendet: Monday, September 07, 2009 4:00 PM An: r-help@r-project.org Betreff: [R] Omnibus test for main effects in the face of aninteraction containing the main effects. R 2.9.1 Windows XP UPDATE, Even my first suggestion anova(fita,fitb) is probably not appropriate as the fixed effects are different in the two model, so I don't even know how to perform the ombnibus test for the interaction! I am fitting a random effects ANOVA with two factors Group which has two levels and Time which has three levels: fita<-lme(Post~Time+factor(Group)+factor(Group)*Time, random=~1|SS,data=blah$alldata) I want to get the omnibus significance tests for each factor and the interaction. I believe I can get the omnibus test for the interaction by running the model: fitb<-lme(Post~Time+factor(Group), random=~1|SS,data=blah$alldata) followed by anova(fita,fitb). How do I get the omnibus test for the main effects i.e. for Time and factor(Group)? I could drop each from the model, i.e. fitc<-lme(Post~ factor(Group)+factor(Group)*Time, random=~1|SS,data=blah$alldata) fitd<-lme(Post~Time+ factor(Group)*Time, random=~1|SS,data=blah$alldata) and then run anova(fita,fitc) anova(fita,fitd) but I don't like this option as it will have in interaction that contains a factor that is not included in the model as a main effect. How then do I get the omnibus test for Time and factor(Group)? Thanks John John David Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) Baltimore, MD 21201-1524 (Phone) 410-605-7119 (Fax) 410-605-7913 (Please call phone number above prior to faxing) Confidentiality Statement: This email message, including any attachments, is for th...{{dropped:9}} ______________________________________________ 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.
