I have data collected from an experiment organized as follows:
Two sites, each with 30 trees. 15 are treated, 15 are control at each site. From each tree, we sample three pieces of the stem, and three pieces of the roots, so 6 level 1 samples per tree which is represented by one of two factor levels (root, stem). Then, from those stem / root samples, we take two samples by dissecting different tissues within the sample, which is represented by one of two factor levels for tissue type (tissue type A, tissue type B). These samples are measured as a continuous variable. Total number of observations is 720; 2 sites * 30 trees * (three stem samples + three root samples) * (one tissue A sample + one tissue B sample). Data looks like this...
ï..Site Tree Treatment Organ Sample Tissue Total_Length 1 L LT1 T R 1 Phloem 30 2 L LT1 T R 1 Xylem 28 3 L LT1 T R 2 Phloem 46 4 L LT1 T R 2 Xylem 38 5 L LT1 T R 3 Phloem 103 6 L LT1 T R 3 Xylem 53 7 L LT1 T S 1 Phloem 29 8 L LT1 T S 1 Xylem 21 9 L LT1 T S 2 Phloem 56 10 L LT1 T S 2 Xylem 49 11 L LT1 T S 3 Phloem 41 12 L LT1 T S 3 Xylem 30
I am attempting to fit a mixed effects model using R and lme4, but am new to mixed models. I'd like to model the response as the Treatment + Level 1 Factor (stem, root) + Level 2 Factor (tissue A, tissue B), with random effects for the specific samples nested within the two levels.
In R, I am doing this using lmer, as follows
fit <- lmer(Response ~ Treatment + Organ + Tissue + (1|Tree/Organ/Sample))
From my understanding (...which is not certain, and why I am posting!) the term:
Specifies that 'Sample' is nested within the organ samples, which is nested within the tree. Is this sort of nesting relevant / valid? Sorry if this question is not clear, if so, please specify where I can elaborate.