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I am trying to run a repeated measures glmm with a fixed intercept at 0 for a longitudinal study calculating the spread of a parasite within different genotypes of Daphnia hosts, and testing for a gxg effect between parasite strain and host strain. What I am curious about is how having a fixed intercept would interact with my random effect terms.

What I have now for my model is:

y <- cbind(infected, uninfected)
glmer(y ~ Days*Genotype-1+(Day|Population), family=binomial, data)

Where Days represent the days I sampled and checked for infected/uninfected, Genotype are my strains of Daphnia and Population are the populations of Daphnia I have in the experiment. I am aware that there is some contention about having a fixed intercept, but I am using it as all populations must have started with no infection. Would this model be appropriate for answering my main question?

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  • $\begingroup$ Shouldn't you have Days listed in the random effect portion of your model instead of Time? Also, is Days a numeric variable or a factor in your model? $\endgroup$ – Isabella Ghement Dec 25 '18 at 22:32
  • $\begingroup$ Yes Day should have been Time instead (edited in original post now), I mixed some variables up. Days is a continuous variable, not a factor. $\endgroup$ – reisen Dec 26 '18 at 5:29
  • $\begingroup$ Does this help stats.stackexchange.com/q/7948/35989 ? $\endgroup$ – Tim♦ Dec 26 '18 at 6:51
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    $\begingroup$ At a minimum, it's always a good idea to fit a model with an intercept just to see whether it's significant and/or its estimate is large. There can be many reasons why data are more consistent with a nonzero intercept than with a zero intercept even when you're absolutely sure the response at zero should be zero. $\endgroup$ – whuber♦ Dec 27 '18 at 18:54
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    $\begingroup$ @Isabella I think you're right--I wasn't paying attention to the fact this is a binomial family with a logit link. $\endgroup$ – whuber♦ Dec 27 '18 at 19:57

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