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
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