Hi Sophie and List,
If you are interested in the treatment contrasts for each level of the
Colony factor (with three levels), it may be better to view your experiment
as a factorial and specify a simple linear model rather than the presently
specified linear mixed model. A random Colony term with only three levels
may also not provide the best estimate for the correlation of colony data
between treatments (i.e., as intraclass correlation) that is taken into
account when testing the general treatment term.
Would this work for you?:
fit_life = lm(last.scan ~ 1 + treatment*colony, data = data)
In case the treatment:colony term is significant you could conduct the
additional pairwise tests of interest.
Best,
Paul
On Mon, 07 Dec 2015 13:02:36 +0200, Sophie Waegebaert <
sophie.waegebaert at gmail.com> wrote:
Hi,
I'm still learning how to use R and I have some trouble making using Tukey
posthoc tests. I have a dataset with 3 colonies (A, B and C). Each colony
is divided into 2 treatments: control and DWV. I want to run a GLM to test
wether there is a difference in life expectancy (last.scan) between the
treatment groups for each of the colonies, but I do not know if I am using
the right strategy.
I have taken 'treatment' as a fixed factor and 'colony' as a random
factor:
fit_life = lmer(last.scan~treatment + (1|colony), data =
data)Anova(fit_life, type = 3) # Type of treatment has a significant
effect on on the life expectancy.
Response: last.scan
Chisq Df Pr(>Chisq)
(Intercept) 106.976 1 < 2.2e-16 ***
treatment 25.373 1 4.724e-07 ***
And this is the code I use to do a Tukey posthoc test:
mcp = glht(fit_life, linfct = mcp(treatment = "Tukey"))
summary(mcp)# DWV treatment significantly changes life expectancy (z =
-9.734, p = < 2e-16)
Is it possible to find the difference for each colony?
Thanks a lot for an explanation or hint!
Cheers,
Sophie
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