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).
Cow ID | Lacatation.Status | Period | Period.Length | Walking | Lying | Standing | Grazing |
---|---|---|---|---|---|---|---|
Cow1 | Milk | Day | 6.64 | 0.47 | 4.41 | 0 | 1.75 |
Cow1 | Dry | Night | 11.99 | 0.33 | 2.01 | 8.30 | 1.34 |
Cow2 | Dry | Day | 9.30 | 0.83 | 5.01 | 0.96 | 2.48 |
Cow2 | Milk | Night | 11.87 | 0.31 | 3.24 | 5.31 | 2.99 |
Cow3 | Milk | Day | 6.64 | 0.47 | 4.41 | 0 | 1.75 |
Cow3 | Dry | Night | 11.99 | 0.33 | 2.01 | 8.30 | 1.34 |
Cow4 | Dry | Day | 9.30 | 0.83 | 5.01 | 0.96 | 2.48 |
Cow4 | Milk | Night | 11.87 | 0.31 | 3.24 | 5.31 | 2.99 |
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.