[R-sig-ME] GLM: Difference between treatment groups for each colony (Tukey posthoc test)

[Sophie Waegebaert]

Hi Paul,

Thank you very much for the hint!

The treatment:colony term is not significant, so I will not include it in
the model.

However, I get the same results for the Tukey posthoc test as I had before.
I am able to say that the DWV treatment significantly reduces the life
expectancy, but I do not know the difference between the colonies. With the
results of the Tukey test I want to make a figure (see attachment), but I
do not know how to do it. The asterisks are based on Tukey posthoc test.

Do you know a way to do this in R?

Thanks a lot!

Cheers,
Sophie



2015-12-07 12:30 GMT+01:00 paul debes <paul.debes at utu.fi>:

> 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
>>
>>         [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
>
> --
> Paul Debes
> DFG Research Fellow
> University of Turku
> Department of Biology
> Itäinen Pitkäkatu 4
> 20520 Turku
> Finland
>
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