[R] Direct (null) hypothesis testing using GLMMs - possible?
Tomer Czaczkes
Tomer.Czaczkes at biologie.uni-regensburg.de
Tue Nov 26 12:27:47 CET 2013
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
More information about the R-help
mailing list