I am new to mixed linear models, so I have a question about them.
I have a plant study (being intentionally vague here to achieve confidentiality) evaluating plant senescence rates (leaf death) in an agricultural field. The experiment was laid out in a randomized complete block design with three replications of 20 genotypes. I then collected NDVI multispectral data from the genotypes. After getting this data, I ground-measured senescence scores for 5 plants set aside for this purpose. I visually measured each leaf for leaf senescence based on a percentage of leaf death. For instance, if the leaf was half-brown, it received a rating of 50. This was done for the first 8 leaves of the canopy for each plant. The scores for each leaf level was then averaged across the 5 plants, giving each plot an average "leaf 1" score, "leaf 2" score, .... , "leaf 8" score.
In order to test how far into the canopy a particular camera would be able to detect leaf senescence, I am interested in using a mixed linear model to see if there are any relationships between genotype, leaf canopy levels, and senescence scores to my NDVI data. This is the equation that I have found in the literature:
Y = Xβ + Zµ + ε
where the NDVI response (Y) is modeled by a set of fixed effects (β) and random effects (µ), and ε is the random error term (http://frontiersin.org/articles/10.3389/fpls.2017.01532/full)
I planned on investigating this by assigning fixed vector β as genotype (factor with 20 levels) and leaf level (factor with 8 levels), while vector µ being assigned to ground-truthed senescence scores (random effects). ε would constitute as the error term. Note: Ground-truth error scores will vary for each plot; for instance the average leaf 1 score could be 100 for any given plot, leaf 2 could be 88, and so on.
Am I building this mixed model correctly? (P.S. I'll be using the R package lme4 for this)