I have added a new example here for clarity, see original question below
Eg. I have 10 schools in 5 countries, ten students from each school is sampled.
Prediction variables: student test marks for Language, Math and Science Response variable: school fee
I want to know what subject (ie Math) correlates with the schools fees.
lmer(fees~math+language+science+(1|country/school)) *each row is a student
But now I have the same fees for students within the same school, and school is added as a random effect. Is this allowed? Should I just take the average subject marks per school and drop the school random effect? See original question below
I have a dependent variable that depends on one of my random effects, as such:
Dep R1 R2 X1 X2 X3 30 a g 4 43 21 30 a g 7 46 18 20 b g 5 31 22 20 b g 4 37 17 60 c h 9 50 26 60 c h 7 34 21 lmer(Dep~X1+X2+X3+(1|R2/R1)) (R2=Genus, R1=Species)
I need the random effect, as I have independent data for each specimen, but I know this setup cannot be correct. Plus some of my models fail to converge. I can use the average values of traits for each R1 and then drop the R1 random effect, but then I lose lots of data.
Can I use a linear mixed effects model for this? or should I be using another technique?
I have since decided to use a phylogeny with a PGLS, because taxonomic level random effects are too rough.
At the moment I am looking into pgls.Ives in phytools to account for within species level variation (see Helmus, M. R., Bland, T. J., Williams, C. K., & Ives, A. R. (2007). Phylogenetic measures of biodiversity. The American Naturalist, 169(3)).