Message-ID: <CAGH66HCiv5jiad+_DM+HP91xP3GOHGa1jft8E=KYSJR=i_RYmQ@mail.gmail.com>
Date: 2015-12-14T10:45:24Z
From: Sophie Waegebaert
Subject: Testing overdispersion Gamma glmer
Hello,
I want to compare mean trip duration (length.in.hours) across treatment
conditions and colonies. I have 3 colonies and 2 treatments. So, one half
of a colony gets a DWV treatment and the other half a control treatment.
When a histogram is made, it is clear that the data is skewed to the rigth.
So, I am using a Gamma distribution.
This is the model: fit_length = glmer(length.in.hours~treatment*colony +
(1|RFID), family = Gamma(link = "log"), data = datashort)
I use the log link and not the identity link, because the AIC is lower.
RFID is the code used for each subject in the colonies.
I want to test for the overdispersion assumption by using the following
code:
> datashort$obs = factor(1:nrow(datashort))> fit_length_obs = glmer(length.in.hours~treatment*colony + (1|RFID) + (1|obs), family = Gamma(link = "log"), data = datashort)
> AIC(fit_length, fit_length_obs) df AIC
fit_length 8 5646.758
fit_length_obs 9 -74390.105
There is a clear difference between the AIC values, but I was wondering
wether -74390 is a realistic value? Is it not very low? Or am I using the
wrong method to control for overdispersion?
Thank you for some help!
Kind regards,
Sophie
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