[R] How to get Greenhouse-Geisser epsilons from anova?

[Skotara]

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,....)
>>>
>>>
>>>
>
>