How to get Greenhouse-Geisser epsilons from anova?

Thank you for your help!
Sorry, for bothering you again..
I still have trouble combining within and between subject factors.
Interactions of within factors and D having only 2 levels work well.

How can I get the main effect of D? I have tried anova(mlmfitD, mlmfit). 
With D having 3 levels I would expect the dfs to be 2 and 33. However, 
the output states 84,24??

As long as the between factor has only 2 levels the between/within 
interactions fit well with SPSS, but if D has 3 levels, the mismatch is 
immense.
If I calculate the within effects with myma having  not 12 subjects from 
one group but for example 24 from 2 groups, the output treats it as if 
all subjects came from the same group, for example for main effect A the 
dfs are 1 and 35. SPSS puts out 1 and 33 which is what I would have 
expected.. ..

Peter Dalgaard schrieb:

Nils Skotara wrote:

Thank you, this helped me a lot!
All within effects and interactions work well!

Sorry, but I still could not get how to include the between factor..
If I include D with 2 levels, then myma is 24 by 28. (another 12 by 
28 for the
second group of subjects.)
mlmfitD <- lm(myma~D) is no problem, but whatever I tried afterwards 
did not seem logical to me.
I am afraid I do not understand how to include the between factor. I 
cannot include ~D into M or X because it has length 24 whereas the other
factors have 28... 

Just do the same as before, but comparing mlmfitD to mlmfit:

anova(mlmfitD, mlmfit, X=~A+B, M=~A+B+C)
# or anova(mlmfitD, mlmfit, X=~1, M=~C), as long as things are balanced


gives the D:C interaction test (by testing whether the C contrasts 
depend on D). The four-factor interaction is

anova(mlmfitD, mlmfit, X=~(A+B+C)^2, M=~A*B*C)

Zitat von Peter Dalgaard <p.dalgaard at biostat.ku.dk>:

Skotara wrote:

Dear Mr. Daalgard.

thank you very much for your reply, it helped me to progress a bit.

The following works fine:
dd <- expand.grid(C = 1:7, B= c("r", "l"), A= c("c", "f"))
myma <- as.matrix(myma) #myma is a 12 by 28 list
mlmfit <- lm(myma~1)
mlmfit0 <- update(mlmfit, ~0)
anova(mlmfit, mlmfit0, X= ~C+B, M = ~A+C+B, idata = dd,
test="Spherical"), which tests the main effect of A.
anova(mlmfit, mlmfit0, X= ~A+C,  M = ~A+C+B, idata = dd,
test="Spherical"), which tests the main effect of B.


However, I can not figure out how this works for the other effects.
If I try:
anova(mlmfit, mlmfit0, X= ~A+B,  M = ~A+C+B, idata = dd,  
test="Spherical")

I get:
Fehler in function (object, ..., test = c("Pillai", "Wilks",
"Hotelling-Lawley",  :
       residuals have rank 1 < 4

dd$C is not a factor with that construction. It works for me after

dd$C <- factor(dd$C)

(The other message is nasty, though. It's slightly different in 
R-patched:

 > anova(mlmfit, mlmfit0, X= ~A+B, M = ~A+C+B, idata = dd,

test="Spherical")
Error in solve.default(Psi, B) :
   system is computationally singular: reciprocal condition number =
2.17955e-34

but it shouldn't happen...
Looks like it is a failure of the internal Thin.row function. Ick!
)

I also don't know how I can calculate the various interactions..
My read is I should change the second argument mlmfit0, too, but I 
can't
figure out how...

The "within" interactions should be straightforward, e.g.

M=~A*B*C
X=~A*B*C-A:B:C

etc.

The within/between interactions are otained from the similar tests of
the between factor(s)

e.g.

mlmfitD <- lm(myma~D)

and then

anova(mlmfitD, mlmfit,....)