I have a dataset for time (hours decimal format) spent carrying out different behaviours of cattle in a day. I'm trying to run a general linear mixed model with two independent variables (lactation status: milk or dry, and period: Day or night) for 4 behaviours (walk, lay, stand, graze).
My response variables (behaviours) are dependent on one another, e.g. if cattle walk 4hrs within day (12hrs) then there's only 8hrs left for remaining variables. Can I use
percent <- (data$Period.Length-data$walking.hrs) to account for the linear relationship of the variables (repeat for each behaviour)? Cow ID would be random effect.
Behaviours have non-normal distribution - should I tranform the data using arcsine-square-root prior to analysis, or leave as is and run a generalised linear mixed model? I'm also reading that a beta regression may be more suitable for this data type?
I want to see what affect period has on behaviours, and what affect lactation status has.