> This looks odd. It is a standard split-plot layout, right? 3
> groups of 13 subjects, each measured with two kinds of Rsp = 3x13x2
> = 78 observations.
Yes, that is right.
>
> In that case you shouldn't see the same effect allocated to
> multiple error strata. I suspect you forgot to dec
Gang Chen wrote:
> Thanks a lot for clarification! I just started to learn programming in
> R for a week, and wanted to try a simple mixed design of balanced
> ANOVA with a between-subject factor
> (Grp) and a within-subject factor (Rsp), but I'm not sure whether I'm
> modeling the data correctl
to
> speak for other people, btw, and I'm happy to stand corrected)
>
>> -Original Message-
>> From: [EMAIL PROTECTED]
>> [mailto:[EMAIL PROTECTED] On Behalf Of Gang Chen
>> Sent: Friday, August 03, 2007 4:01 PM
>> To: Peter Dalgaard
>> Cc: r
other people, btw, and I'm happy to stand corrected)
> -Original Message-
> From: [EMAIL PROTECTED]
> [mailto:[EMAIL PROTECTED] On Behalf Of Gang Chen
> Sent: Friday, August 03, 2007 4:01 PM
> To: Peter Dalgaard
> Cc: r-help@stat.math.ethz.ch
> Subject: Re: [R]
Thanks for the response!
It is indeed a balanced design. The results are different in the
sense all the F tests for main effects are not the same. Do you mean
that a random interaction is modeled in the aov command? If so, what
would be an equivalent command of aov to the one with lme?
Than
Gang Chen wrote:
> I have a mixed balanced ANOVA design with a between-subject factor
> (Grp) and a within-subject factor (Rsp). When I tried the following
> two commands which I thought are equivalent,
>
> > fit.lme <- lme(Beta ~ Grp*Rsp, random = ~1|Subj, Model);
> > fit.aov <- aov(Beta ~ R
I have a mixed balanced ANOVA design with a between-subject factor
(Grp) and a within-subject factor (Rsp). When I tried the following
two commands which I thought are equivalent,
> fit.lme <- lme(Beta ~ Grp*Rsp, random = ~1|Subj, Model);
> fit.aov <- aov(Beta ~ Rsp*Grp+Error(Subj/Rsp)+Grp,
