# Should I transform my count response variable to continuous response variable for mixed models (glmms)?

I conducted an experiment where I counted the number of infected leaves per plant. There were four replicate plants per treatment (total two treatments). So my response variable is positive count data, ranging from 0-392. I'm going to use generalized linear mixed models to see the effect of weather variables and mulching status on disease development (number of infected leaves). My question are,

1. Should my response variable be number of infected leaves PER PLANT or on FOUR PLANTS (there were four plants per treatment)?

2. Should my response variable be count data or continuous? That is, should I use number of infected leaves per plant/per treatment (count data), or should I take an average of number of infected leaves per plant/per treatment (question 1), and thus use a continuous response variable? Total number of leaves per plant had not been counted, but an average of 3132 total leaves per plant are normally used in the literature for this plant species (Justin Brouwers boxwoods).

If you're interested in the details of the experiment, I left out my potted plants in the field for a week, took them back to the glasshouse and counted the number of infected leaves per plant after two weeks. I had two treatments - mulched (a practice where diseased plants materials left from previous years are covered with straw to prevent fungus spores splash dispersal to plants during rain), and non-mulched (infected plant remains from previous years are not covered, so we'd expect more spores being splashed to plants during rain. Disease is only spread by rain). The experiment was conducted over two years. In the first year, plants were taken out to the field for 30 weeks and in the second for 26 weeks, so total 56 weeks. If I use average or number of infected leaves PER TREATMENT (four plants), I'll have 56 data points. If I use number (or average) of infected leaves PER PLANT than I will have 56 x 4 = 224 data points. I have a total of 8 predictors ( mulched, non-mulched, wind speed, wind direction, total rain, rain duration, relative humidity and temperature during each week plants were in the field). Thanks very much for any assistance.

• Do you also know the number of non-infected leaves on each plant? For example, having 4 infected leaves out of 4 total would seem to be a lot worse than having 4 infected leaves out of 400. Please add that information by editing the question, as comments are easy to overlook and can be deleted.
– EdM
Commented Dec 28, 2022 at 18:12
• @EdM Thanks for your comment. I have edited question 2 with this information. Total number of leaves were not counted, but an average of 3132 total leaves per plant are normally used in the literature for this plant species.
– Ahsk
Commented Dec 29, 2022 at 13:37

As you did not use the same plants repeatedly, think carefully whether a mixed model is really appropriate or helpful. With data over only 2 years, it doesn't make sense to treat the calendar year as a random effect. Whether a random effect for location makes sense similarly can depend on the number of locations. It's often thought best to have on the order of 6 groups (locations, here) to treat them as a random effect; see this page and this page for some discussion. You don't seem to have that many locations.
You propose to use the week as a random effect, but I suspect that (as typical for studies over time) you will have correlations in effects from week to week that a simple random intercept as you propose for week wouldn't adequately handle. It can be better to model week as a continuous variable but flexibly, with a regression spline or with smoothers in a generalized additive model.