Thank you very much Ista. Can you please be a bit more specific as to using the overall error. I don't know how to actually do it in R. thank you, Or.
On Sun, Dec 20, 2009 at 7:09 PM, Ista Zahn <istaz...@gmail.com> wrote: > Hi Or, > I understand your question (and am not sure what the confusion is > about actually). Just like you said, you want to know the effect of A > at B=1, and the effect of A at B=2. In this case, you want to know if > drug has a significant effect for those with strain one, and whether > drug has a significant effect for those with strain two. So the good > part is, I understand the question. > > > The bad part is that unfortunately I don't know how to do it with > aov(). Also, I'm not a stats guru and could easily be wrong about the > following advice. You've been warned! > > If you didn't have a mixed model, I would tell you to run the model > using lm() twice, setting drug = 1 as the reference group the first > time, and drug = 2 as the reference group the second time, but this > won't work in your case. The best I can offer is a suggestion to run > the model separately for each level of drug. Note that you can and > should use the error term from the overall model though -- you will > have to do this "by hand" (just divide MS_effect from the subset > model by MS_error from the full model, and evaluate using df_error > from the overall model). So basically, I'm suggesting that you do > > Data.drug1 <- subset(Data, drug == "1") > aov.model.drug.1 <- aov(dependent~(exposure*strain) + > Error(subject/exposure) + (strain), data=Data.drug1) > summary(aov.model.drug.1) > > Data.drug2 <- subset(Data, drug == "2") > aov.model.drug.2 <- aov(dependent~(exposure*strain) + > Error(subject/exposure) + (strain), data=Data.drug2) > summary(aov.model.drug.2) > > Good luck! > > -Ista > > On Sun, Dec 20, 2009 at 11:35 AM, Or Duek <ord...@gmail.com> wrote: > > For some reasion I wasn't able to use TukeyHSD - I think because I need > to > > set the different levels under a second variable. > > Tukey only helps me when I have more than 2 levels of same variable. > > Thanl you. > > On Sun, Dec 20, 2009 at 6:32 PM, S Devriese <sdmaill...@gmail.com> > wrote: > > > >> On 12/20/2009 04:56 PM, Or Duek wrote: > >> > I don't have missing data. > >> > about what I need. > >> > Lets say the drug*strain interaction is significant - now I want to > check > >> > for drug under the levels of strain - compare drug 1 and 2 only on > strain > >> 1 > >> > and then only on strain 2. > >> > Or I'd like to compare the strains under levels of exposure. > >> > This is the kind of data I fail to see in summary() but it is > important > >> to > >> > understand the interactions. > >> > thank you. > >> > > >> > >> Do you main pairwise multiple comparison tests like Tukey Honest > >> Significant Difference tests? Then you could use TukeyHSD in the stats > >> package or see the DTK package (Dunnett-Tukey-Kramer Pairwise Multiple > >> Comparison Test Adjusted for Unequal Variances and Unequal Sample Sizes) > >> > >> Stephan > >> > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > 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. > > > > > > -- > Ista Zahn > Graduate student > University of Rochester > Department of Clinical and Social Psychology > http://yourpsyche.org > [[alternative HTML version deleted]] ______________________________________________ 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.
