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
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