hello,
yes, thanks a lot - i noticed relevel() beeing very convinient for this
purpose.
having an authority at hand may i kindly ask, if you could reinsure me
that the contrasts below are set up correctly, supposing i want to test
the earlier mentioned hypotheses simultanously.
thanks,
kay
Should you need to do it again, you may want to look at the relevel
function. I suppose that would meet the definition of some versions of
"on the fly" but once I have a model, rerunning with a different
factor leveling is generally pretty painless.
--
David.
On Oct 15, 2010, at 9:09 AM,
..by some (extensive) trial and error reordering the contrast matrix and the
reference level
i figured it out myself -
for anyone who might find this helpful searching for a similar contrast in
the future:
this should be the right one:
c2<-rbind("fac2-effect in A"=c(0,1,0,0,0,0,0,0),
hello,
i was shortly asking the list for help with some interaction contrasts (see
below) for which
i had to change the reference level of the model "on the fly" (i read a post
that this is possible in
multcomp).
if someone has a clue how this is coded in multcomp; glht() - please point
me ther
hello list,
i'd very much appreciate help with setting up the
contrast for a 2-factorial crossed design.
here is a toy example:
library(multcomp)
dat<-data.frame(fac1=gl(4,8,labels=LETTERS[1:4]),
fac2=rep(c("I","II"),16),y=rnorm(32,1,1))
mod<-lm(y~fac1*fac2,data=dat)
## the
On Nov 2, 2009, at 8:40 AM, Maya Pfaff wrote:
Dear R experts
I am running a negative-binomial GLM (glm.nb) to test the null
hypotheses
that species 1 and 2 are equally abundant between site 1 and site2,
and
between each other. So, I have a 2x2 factorial design with factors
Site
(1,2) an
Dear R experts
I am running a negative-binomial GLM (glm.nb) to test the null hypotheses
that species 1 and 2 are equally abundant between site 1 and site2, and
between each other. So, I have a 2x2 factorial design with factors Site
(1,2) and Taxon (1,2).
Since the Site:Taxon interaction is signif
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