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,....)
Do you know what to do?
Thank you very much!
Peter Dalgaard schrieb:
Skotara wrote:
Dear all,
I apologize for my basic question.
I try to calculate an anova for repeated measurements with 3 factors
(A,B,C) having 2, 2, and 7 levels.
or with an additional fourth between subjects factor D.
Everything works fine using
aov(val ~ A*B*C + Error(subject/ (A*B*C) ) ) or
aov(val ~ (D*A*B*C) + Error(subject/(A*B*C)) + D )
val, A, B, C, D and subject are columns in a data.frame.
How can I get the estimated Greenhouse-Geisser and Huynh-Feldt epsilons?
I know Peter Dalgaard described it in R-News Vol. 7/2, October 2007.
However, unfortunately I am not able to apply that using my data...
Why? It is supposed to work. You just need to work out the X and M
specification for the relevant error strata and set test="Spherical"
for anova.mlm, or work out the T contrast matrix explicitly if that
suits your temper better.
Furthermore, I am still confused of how SPSS calculates the epsilons
since it is mentioned that perhaps there are any errors in SPSS??
I would be glad if anyone could help me!
I am looking forward to hearing from you!
Thank you!
Nils
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