How to use glmer for nested data in R?
Given that my pathogen diversity data is count data with many zeros, which is why I have been exploring using using a GLMM with the lme4::glmer command in R to analyze the data. For the analysis I want to treat latitude as a numeric fixed factor and site as a random factor nested with location.
Which is an example of a nested random effect?
Nested random effects. Nested random effects assume that there is some kind of hierarchy in the grouping of the observations. E.g. schools and classes. A class groups a number of students and a school groups a number of classes. There is a one-to-many relationship between the random effects. E.g.
How to code nested and crossed random effects in lme4?
Statistician in daylight, chiropterologist after sunset. But often things get mixed up. People often get confused on how to code nested and crossed random effects in the lme4 package. I will try to make this more clear using some artificial data sets.
What are the nested factors in a mixed model?
This is a skeletal post to show the equivalency of different ways of thinking about “nested” factors in a mixed model. The data are measures of life history traits in lice that infect salmon. The treatment is the source of lice – from farmed raised or wild salmon.
How to use glmer for mixed effect logistic regression?
In glmer you do not need to specify whether the groups are nested or cross classified, R can figure it out based on the data. We use the same (1 | ID) general syntax to indicate the intercept (1) varying by some ID. For models with more than a single scalar random effect, glmer only supports a single integration point, so we use nAGQ=1.
What does model <-glmer ( cbind ) do?
I note that model<-glmer (cbind (success,n_subplots)~FENCED*seedling_size + (1|FENCED:location) does exactly the same thing. The usage of different syntax to do the same thing is what helped to confuse me in the first place.
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.