A common problem in animal studies is pseudoreplication of data points due to a limit in the number of animals available in a study population.
I need to address any pseudoreplication and influence of individual in my study. I had planned to model ID (individual animal) as a random factor in a glmr.
In my main study, with a large data set I am using permutation based logistic regressions for whether animals of a certain age class are underweight. Looking at a model :
mod1 <- glm(Underweight ~ Age.Class, family = binomial(), data = data)
against (likelihood ratio test) a permuted data set of the same data, this finds Age class predicts tendency to be underweight.
To factor in ID into this, I assign individual identity to a subset of my data (was not possible time wise to ID whole data set) I had planned to address pseudo replication as a post hoc assessment on the work with this subset of data.
I am confused as to the method for this, would I:
a) do a simple LRT test between a model that factors in ID and doesn't, if there is no sig difference then ID has no effect, i.e.:
glm1 <- glmer(Underweight ~ Age.Class + (1 | ID.number), family = binomial(), data = data) glm2 <- glmer(Underweight ~ Age.Class, family = binomial(), data = data) lrtest(glm1, glm2)
b) Do I also need to interpret ID into some kind of permutation test like the original model?