My model is currently setup as follows either with just random intercepts:
OR with random slopes as well
Treatment being + or -
Exposures ranging from 1 to 6 with one exposure per day, ever day for 6 days.
Seedling_ID being a number for each individual plant to pull out the within subjects random effects.
And Log10.Daily_Proportion_Growth. being the log10 ratio of their size at Exposure X to their size at Exposure X-1.
The analogous Log10.Cumulative_Proportion_Growth is simply the log10 ratio of their size at Exposure X to their initial size (Exposure 0).
where I'm interested in the Daily growth, and Cumulative growth of some plants in response to a treatment, and I've observed that there is variation in the effect of the treatment depending on the exposure (see below, which is why I wanted time as categorical and not continuous).
My model is exactly the same for the Cumulative growth, treating Exposure (time) as categorical on account of the unequal effect of treatment across exposures, with the same question: Random slopes or just random intercepts?
I've been trying to hunt down this answer on stack-exchange on my own, but the examples never quite seem to fit particular situation, or I get conflicting or difficult to interpret (for me!) answers. As someone with no experience publishing the results of mixed effects models, I wanted to be sure I wasn't mangling the process!