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 <istazahn@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 <orduek@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 <sdmaillist@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
>

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