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How is nested data used in glmer Stack Overflow?
I have data on the diversity of pathogens infecting a particular host species across latitudes. The design involved collecting 20 individuals at 3 sites within 4 locations of different latitudes, therefore I have 20 individuals, nested within 3 sites, nested within 4 locations.
How to run a glmer with paired data and repeated measures?
A site consist of 1 exclosure + 1 control plot. There are 15 sites (so 15 exclosures + 15 paired plots = 30 experimental units). Each experimental units has is unique “id”. The abundance (proportion : continuous value between 0 and 1) of fir has been monitored for 5 years (once every year = repeated measures).
Is it possible to add correlation parameters to glmer?
Additionally, it is not possible to insert a Correlation parameters in glmer. Adding the repeated measures (if it was possible) would mostly make the statistical test a bit more powerful, but would not change the general output. And since I get significant results with this model, I will just leave it. Still, thank you very much for your help !
How are repeated measures nested in an experimental unit?
Each experimental units has is unique “id”. The abundance (proportion : continuous value between 0 and 1) of fir has been monitored for 5 years (once every year = repeated measures). So the repeated measures is nested inside the “id”.
Which is an example of nested random effects?
The following is an example of specifying nested random effects. The example will use the following variables. The random effect B is nested in the random effect A. The population is the unique levels of A interacted with B. The pbDat data set does not contain crossed and nested random effects.
How are fixed effects nested within fixed effect stack?
To avoid pseudo-replication, this is modeled at the plot level as a binomial with x successes (x can be an integer [0:4]) in 4 trials. I have two fixed effects that I am interested in: Fencing and average seedling size.
How to include nesting factor in a GLMM?
In the case of balanced data, this can be written equivalently N n 1 > N k or n 1 > k. In other words you need to have less random parameters than the number of observations in each cluster/group, subject in your case. This cannot be the case if you add the group/task term in the random part.