You should post this on the r-sig-mixed-models list, not here. Cheers, Bert
Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Mon, Sep 3, 2018 at 7:43 AM Pedro Vaz <[email protected]> wrote: > We did a field study in which we tried to understand which factors > significantly explain the probability of a group of animals (5 species in > total) crossing through 30 wildlife road-crossing structures. The response > variable is binomial (yes=crossed; no = did not cross) and was recorded by > animal species. We did about 30 visits to each crossing structure (our > random factor) in which we recorded the binomial response by each animal > species and the values of a few predictors. > > So, I have this (simplified for better understanding) mixed effects model: > library (lme4) > > Mymodel <- glmer(cross.01 ~ stream.01 + width.m + grass.per + (1| > structure.id), > data = Mydata, family = binomial) > > stream is a factor with 2 levels; width.m is continuous; grass.per is a > percentage > > This is the model in which I assessed crossings by all species combined > (i.e., cross. 01 = 1 when an animal of any species crossed, cross.01 = 0 > when no animal crossed). However, we did one model per species and those > species-specific models highlight that different species exhibit different > relationships between crossings and explanatory variables. > > My problem: This means that my model above suffers from an additional > source of variation related to the species level without accounting for it. > However I cannot recalibrate the above model adding the species level as > random factor because, in my binomial response, the zero means no species > crossed (all zeros would have "NA" or, say, "none" for species) and so that > additional source of variation is only present when the response was 1. > Just to confirm this, I did add species as a random factor: > > (1 | structure.id) + (1 | species) > > As expected, the message is "Error: Response is constant" > > How can I account for the species variability in my model in lme4? > > A few more details: > A few more details: > - I had 5 mammal species crossing through the 30 road-crossing structures. > In 134 occasions (i.e., 134 of my records on individual > crossing-structures), no animal crossed (so, @Dimitris Rizopoulos, no, I > didn't have the species of the animals which did not cross. A "no cross" > was a "zero" for that visit to the crossing-structure). In 498 occasions, > at least one animal of a given species crossed the structure (these were my > "ones" in my logistic response) > - A side comment: This is to respond to a reviewer in a paper of mine, > i.e., I did and presented species-specific and "all combined species" > models in the draft reviewed but now the reviewer is asking me to control > for the species variability in the "combined species model". He asked me to > include a random factor but I realized that is not possible since all my > zeros would have "none" for the species that crossed. So, is it possible to > control for the species variability in my model in lme4 in another way? I > know in nlme including a fitting of variance structures it's not that > difficult... > - Every time an animal crossed, the binary response was "one" and I > recorded the animal species as well. Thus, I have variability between > species in the "ones" but not in my "zeros" of my logistic model. > > [[alternative HTML version deleted]] > > ______________________________________________ > [email protected] mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ [email protected] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
