Dear R gurus,
I have the following model:
appcov.aov - aov(yield ~ prevyield + trt + block)
where prevyield is a continuous numeric covariate and trt and block are
factors (yes, I did factor()!)
Now, when I do a TukeyHSD, my diff's are all screwed up!
For instance:
treatment mean for treatmen E
is in pounds... but I
don't get why the means of the models with and without covariate would
change. The SE's are another story, but the means?
Thanks
Peter Dalgaard wrote:
Damián Cirelli [EMAIL PROTECTED] writes:
Dear R gurus,
I have the following model:
appcov.aov - aov(yield ~ prevyield + trt
I have interactions where I shouldn't so nevermind, I'm a dumb ass.
Thanks again
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I think Ross Robert should be consulted...
Tux was born because Linus happend to say in a forum that he was rather
fond of penguins ;-)
May be these guys have a favourite animal... and it would be cool if
each had a different favourite animal and thus one mythic beast could
be created from
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
contrasts with aov().
If I recall correctly, following Prof Ripley's suggestion led aov() to accept
the design was balanced, but model.tables() still did not (but that could have
been my error). However, se.contrast() worked.
Cheers
Peter Alspach
Damián Cirelli [EMAIL PROTECTED] 02
Try this:
r - sort(rank(a, ties.method=average), decreasing=T)
Jose Quesada wrote:
Hi,
Is there any simple solution to get ranks in descending order?
Example,
a - c(10, 98, 98, 98, 99, 100)
r - rank(a, ties.method=average)
produces
1 3 3 3 5 6
I would want this instead:
6 5 3 3 3 1
Note that
