I have a question going in the same direction as this one: Restricted Permutations
However, I have a dataset of multiple Locations from which I have samples of sometimes 5 sometimes only 3 years. Not all locations were sampled in every year. I ended up having a dataset with observations e.g. several allel frequncies and a dataset with explanatory variables for every location. The second dataset contains information about soil characteristics, ownership of the fields and other agronomicaly relevant factors.
I want to use the second dataframe to explain the variance of my observations and came across RDA which seems appropriate. However, I don't know whether I can account for the repeated measures design in RDA or whether to use other statistical approaches in that regard.
I would like to control for the repeated measures design, while being aware of the missing data. I don't know whether this matters.
Second part of the question: I would like to do the analysis in
R. Is the
vegan package the best choice for this or are there alternative options? Also, do the observations need to be sorted by site within every year and do I need to put
Location as a conditional factor? I know I can add conditional variables in RDA together with constrained variables but would it be sufficient to account for Year and Location as conditional variable?
I was approaching the problem with R and package vegan in the following way but is this the correct application?