[R] Direct (null) hypothesis testing using GLMMs - possible?

[Tomer Czaczkes]

Dear Forumites,



Hi, I'm a long time eavesdropper, first time poster, but I simply couldn't

find any answer to this perhaps rather naive question:



I am trying to see if my data is significantly different from a null

hypothesis using GLMMs.

I would like to run a GLMM because I have random effects. In the future I

might want to do a similar thing with a non-Gaussian distribution structure

as well.



In my current example, I have a series of proportions - in this case the

proportion of ants on one of two available paths. My null-hypothesis is 0.5:

that the ants choose a path randomly, so there will be a more or less amount

of ants on both paths at any given time.



The only way I could think of doing this would be to make a dummy dataset

with a mean of 0.5 and a reasonable variance, put both the dummy data and

real data into one dataframe, and test whether data type (dummy or real) is

a significant predictor of "proportion of ants choosing side X".



 Is there any more elegant way of doing this with a GLMM? Alternatively, can

anyone suggest an alternative way to do such a thing? I will want to add

interactions to the model as well. I generally use the LME4 package, and the

lmer function.





Many thanks for you attention, and I hope my first foray into forum-posting

wasn't hopelessly naive or misplaced...



Tommy

---

University of Regensburg


Dr. Tomer J. Czaczkes
University of Regensburg