[R] aov or lme effect size calculation

[Greg Trafton]

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: r-help-bounces at r-project.org
>> [mailto:r-help-bounces at r-project.org] On Behalf Of Greg Trafton
>> Sent: Tuesday, September 02, 2008 10:25 AM
>> To: r-help at 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
>>
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