Dear Rula
That is really a statistical question not one for this list but the
answer is that the fact that they are all zero for that category
explains it. Search on-line for separation for more details.
Michael
On 18/01/2019 09:56, Rula DomÃnguez wrote:
Hello to everyone,
after much reading I decided to write because I cannot find a solution to
my question.
I already did a priori contrasts before for a continuous variable with
normal distribution. Now I have another variable (burrow), which is
binomial, and I can do the GLM for it. But when I do the a priori
contrasts, it has no result in the cases where all data are 0 (is not that
there are no data, they are just all 0 in a category (treat 30-30), and I
want to compare this with others that have ones).
Data sructure is like this:
head(burrow)
date day treat psu sp burrow
1 3 0 30-30 36 B 0
2 3 0 30-30 36 B 0
3 3 0 15-30 36 B 1
4 3 0 15-30 36 B 1
5 3 0 15-30 36 B 1
6 3 0 10-25 36 B 1
My model is this:
model4B2<-glm(burrow~ treat, family=binomial(link="logit"), data=D4B)
And I did the contrast like this:
require(multcomp)
#Test contrastes 30 vs all (there are 4 categories to compare)
k3010R1<-matrix(c(3,-1,-1,-1),1)
k3010R1
t3010<-glht(model4B3.2,linfct=k3010R1)
summary(t3010)
But is not working and I am sure it should work.
Could it be because my explanatory variable is cathegorical?
Or is just not possible to do contrasts for binomial when you have all 0 in
some cathegory?
Thank you in advance,
--
Michael
http://www.dewey.myzen.co.uk/home.html
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