Dear mixed-modelers, I am analyzing seedling survival (0/1) as a function of the density of neighbors with a different level of relatedness to the focal species. This is, the density of neighbors of the same species, of the same genus, family and so on. Focal species rarely ocurr with neighbors of the same genus so 92% of the cases correspond to 0 neighbors of the same genus (the frequency table of the density of neighbors of the same genus is copies at the end).I have ran a model using glmer where both the Wald Z test and the likelihood ratio test of the models, with and without the fixed effect, show a significant increase of the seedling survival odds with an increase in the density of neighbors from the same genus.I have found lots of information discussing problems with response variables containing many zeroes but almost nothing about predictors with many zeroes. In a book by Simon Sheater (2009) on regression with R there is a brief section that discusses the transformation of predictors in logistic regression for binary data. Sheater (2009) shows how there is a need to transform skewed variables to mantain the linear relationship between the predictor and the log odds. He also shows that when the predictor variable has a poisson distribution the log odds remain a linear funtion of the predictor. My variable in question cannot be normalized with any transformation and does not precisely follows a poisson distribution.In classic regression my data would certainly invalidate the analysis but I am wondering if this is also the case for mixed models fit by glmer. Thank you very much for your attention to this problem,Edwin Frequency table for the density of neighbors from the same genus
Density (m2)
Count
0
33403
1
1811
2
505
3
248
4
76
5
57
6
24
7
14
8
10
9
10
10
8
11
27
12
1
13
1
14
2
16
1
17
1
18
1
19
1
20
1
21
3
23
7
24
1
27
3
29
1
33
1
34
2
35
1
50
2
56
1
80
1
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