Greg
Upgrade your packages to the supported versions (lme4 and Matrix), and
use lmer and not lme.
### Example
> example(lmer)
> anova(fm1)
Analysis of Variance Table
Df Sum Sq Mean Sq F value
Days 1 30032 30032 45.854
Your method for eta-squared with a mixed model is another story,
however.
> -----Original Message-----
> From: Greg Trafton [mailto:[EMAIL PROTECTED]
> Sent: Tuesday, September 02, 2008 1:57 PM
> To: Doran, Harold
> Cc: r-help@r-project.org
> Subject: Re: [R] aov or lme effect size calculation
>
> Sorry about that. My problem is computational, not
> statistical and exactly as you say: I don't quite know how
> to get the correct variance component from either aov or lme.
> the way to compute partial eta squared is:
>
> partial-eta-squared = SS(effect) / (SS(effect) + SS(error))
>
> AOV gives Sum Squares for both effects and the interaction,
> but lme doesn't even give that in default format.
>
> thanks,
> greg
>
> On Sep 2, 2008, at 11:43 AM, Doran, Harold wrote:
>
> > Greg
> >
> > You haven't really explained what your problem is. If it is
> conceptual
> > (i.e., how do I do it) this is not really the right place
> for in-depth
> > statistical advice, but it is often given. OTOH, if your problem is
> > computational, please explain what that is? For example, maybe you
> > know how to compute eta-squared, but you want to extract
> the variance
> > component and you can't figure that out.
> >
> > Without more info, it is hard to help. Now, with that said,
> with lme
> > (or mixed models) you have multiple variance components, so
> how would
> > you go about computing eta-squared anyhow?
> >
> >> -----Original Message-----
> >> From: [EMAIL PROTECTED]
> >> [mailto:[EMAIL PROTECTED] On Behalf Of Greg Trafton
> >> Sent: Tuesday, September 02, 2008 10:25 AM
> >> To: r-help@r-project.org
> >> Subject: [R] aov or lme effect size calculation
> >>
> >> (A repost of this request with a bit more detail)
> >>
> >> Hi, All. I'd like to calculate effect sizes for aov or
> lme and seem
> >> to have a bit of a problem. partial-eta squared would be my first
> >> choice, but I'm open to suggestions.
> >>
> >> I have a completely within design with 2 conditions (condition and
> >> palette).
> >>
> >> Here is the aov version:
> >>
> >>> fit.aov <- (aov(correct ~ cond * palette + Error(subject),
> >> data=data))
> >>> summary(fit.aov)
> >>
> >> Error: subject
> >> Df Sum Sq Mean Sq F value Pr(>F) Residuals 15
> >> 0.17326 0.01155
> >>
> >> Error: Within
> >> Df Sum Sq Mean Sq F value Pr(>F)
> >> cond 1 0.32890 0.32890 52.047 4.906e-09 ***
> >> palette 1 0.21971 0.21971 34.768 4.447e-07 ***
> >> cond:palette 1 0.50387 0.50387 79.735 1.594e-11 ***
> >> Residuals 45 0.28437 0.00632
> >> ---
> >> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> >>
> >> and here is the lme version:
> >>
> >>> fm1 <- lme(correct ~ cond * palette, random=~1 | subject,
> >> data=data) > anova(fm1)
> >> numDF denDF F-value p-value
> >> (Intercept) 1 45 4031.042 <.0001
> >> cond 1 45 52.047 <.0001
> >> palette 1 45 34.768 <.0001
> >> cond:palette 1 45 79.735 <.0001
> >>
> >> Thanks so much!
> >> Greg
> >>
> >> ______________________________________________
> >> 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.
> >>
>
>
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