Dear r-helpers,
I am looking at a designed experiment in which one predictor variable
has 5 levels (0, ..., 4) and the other has 6 levels (1.1, ..., 1.6),
with 33 observations per cell. This design was given to 13 subjects.
0 1 2 3 4
1.1 32 33 0 0 0
1.2 33 33 33 0 0
Hi all,
I'm new to R and have the following problem:
I have a 2 factor design (a has 2 levels, b has 3 levels). I have an
object kidney.aov which is an aov(y ~ a*b), and when I ask for
model.tables(kidney.avo, se=T) I get the following message along with
the table of effects:
Design is
Damián
I asked a similar question a few months ago (3 August 2004):
temp.aov - aov(S~rep+trt1*trt2*trt3, data=dummy.data)
model.tables(temp.aov, type='mean', se=T)
Returns the means, but states Design is unbalanced - use se.contrasts
for se's which is a little surprising since the design
Thanks Peter,
I still wonder why it thinks it's unbalanced...
The se's of the contrasts are different than the se's of the means,
which is the point of se=T in model.tables (type means) I would have
thought. No big deal though, the following code makes a nice table with
the se's of the means
01, 2004 2:03 PM
To: [EMAIL PROTECTED]
Subject: Re: [R] unbalanced design
Thanks Peter,
I still wonder why it thinks it's unbalanced...
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PLEASE do read the posting
Hello,
I'm wondering what's the best way to analyse an unbalanced design with a low number of
replicates. I'm not a statistician, and I'm looking for some direction for this
problem.
I've a 2 factor design:
Factor batch with 3 levels, and factor dose within each batch with 5 levels. Dose
