Yoav Avneon <Avneony <at> bgu.ac.il> writes: > I have conducted an experiment in order to examine predation pressure in the > surroundings of potential wildlife road-crossing structures. I have > documented predation occurrence (binary…) in these structures and calculated > several possible explanatory variables describing the spatial heterogeneity > in several scales. At the landscape scale I have calculated the percentage > of different land-uses (7) in buffers around the structures with changing > radii (100m, 250m, 500m, 1000m and 2000m). For each radii I have generated > a set of all possible models from the given 7 variables related to this > radii. > I have tried to account for random effects. A spatial random effect – the > structure itself as a repeated measurement, by adding the term > (1|Road_Structure). A temporal random effect – the observation session (is > it correct to say that this examines possible learning in time ?), by adding > the term (1|Session.ord). > I have generated 4 sets for each radii: 1) with both random effects, 2+3) > only one (each) random effect, and 4) none. > I have tried to model the relationships using glm, glmer and lmer. > The problem is that in the full model (all 7 variables) of some sets with > random effects I get the following error: > *Error in mer_finalize(ans) : Downdated X'X is not positive definite, 8.* > > The only record of this error I have found in the internet is for data with > missing observation. This doesn't comply with my data set. I think it > might be a singularity problem but I don't know how to check it, and as a > result, I obviously don't know how to fix it. > I have attached example R scripts (for the radius 2000) and the data table. > Any help is highly appreciated ! > Thanks and all the best,
Questions like this (and follow-ups) should probably go to r-sig-mixed-mod...@r-project.org. I don't have time to dig into this in detail, but I would start by (1) centering and scaling all of your variables (see ?scale, and Schielzeth 2010 _Methods in Ecology and Evolution_ and (2) checking the correlations among your variables; if they are strongly correlated you have lots of options (none of them perfect), much discussed on the internet, including discarding some variables or using PCA. ______________________________________________ R-help@r-project.org mailing list 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.
