Hi,
yet another anova.mlm problem - it doesn't seem to end.
This time, I have a setup with a few within-subject factors and a
between-subject factor (SGROUP). Consider the most simple case with only one
within-factor (apo):
> mlmfit0 <- lm(data.n ~ 0 + SGROUP)
> mlmfit1 <- lm(data.n ~ 1 + SGROUP)
Sorry, forgot to mention:
I'm using R 2.1.0 on a x86 Debian Sarge system. I can reproduce the behaviour
on a similar system.
Unfortunately, I don't have any other OS with R installed available to me.
Thanks again,
Bela
--
Bela Bauer - [EMAIL PROTECTED]
PGP 0x97529F5C
http://www
ova.mlmlist(object = mlmfit, mlmfit0, test = "Spherical") :
Object "mlmfit" not found
What causes mlmfit to "disappear" between two lines? I haven't got the
slightest clue where to look for an answer...
Thank you very much for your help.
Bela Bauer
--
Weiters
Hi,
I'm using anova.mlm sphericity tests/corrections, and I'm getting
different values than SAS. In order to be able to use these values for
publications, I'd need to know more about the SAS bug that is mentioned
in the Reference Manual.
- What exactly causes the different values?
- Is it just a
Hi,
sorry to bother you again, but I can't figure it out myself and I also
can't find any in-depth documentation about it...
Consider the following SAS code (A1II2... contain the measurements for
40 subjects):
proc glm;
model
A1II2
A1IN2
A1NI2
A1NN2
= /nouni;
repeated CONTEXT 2, TARGET_SATZ 2;
Hi,
I'm still not quite there with my H-F (G-G) correction code. I have it
working for the main effects, but I just can't figure out how to do it
for the effect interactions. The thing I really don't know (and can't
find anything about) is how to calculate the covariance matrix for the
interaction
Hi,
I'm still not quite there with my H-F (G-G) correction code. I have it
working for the main effects, but I just can't figure out how to do it
for the effect interactions. The thing I really don't know (and can't
find anything about) is how to calculate the covariance matrix for the
interact
Peter Dalgaard wrote:
However, the real crucial thing here is that SAS is de facto fitting a
mixed-effects model, with random effects being everything with Subject
inside. So, except for the GG/HF business, you should get something
similar if you try
summary(aov(vecData ~ facCond*facRoi + Error(fac
Bela Bauer wrote:
A quick addition:
I've read https://stat.ethz.ch/pipermail/r-help/2000-November/007457.html
but I really can't get around using those mechanisms because it will
already be quite a transition for users to go from SAS to R...and I
can't give them different res
Hi,
I'm still trying to figure out that GLM procedure in SAS.
Let's start with the simple example:
PROC GLM;
MODEL col1 col3 col5 col7 col9 col11 col13 col15 col17 col19 col21 col23
=/nouni;
repeated roi 6, ord 2/nom mean;
TITLE 'ABDERUS lat ACC 300-500';
That's the same setup that I had in my la
Peter Dalgaard wrote:
Bela Bauer <[EMAIL PROTECTED]> writes:
>My suspicion would be that it has something to do with calculating the
>correction terms before or after contrast transformations (there must
>be a coordinate-free version of the corrections?), but I can't grok
>
re
referring to. Could you forward the relevent messages or the code to me?
Thanks again
Bela Bauer
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wo code versions,
but I don't know for sure.
Now, with the difference between my value and the one from SAS being so
small, I suspect that there's only a very slight difference between the
algorithms. Do you have any hints what these could be, or how I could go
about investigating it
Hi,
I'm currently working on porting some SAS scripts to R, and hence need
to do the same calculation (and get the same results) as SAS in order to
make the transition easier for users of the script.
In the script, I'm dealing with a two-factorial repeated-measures anova.
I'll try to give you a
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