Hi,
By request of Prof. Bolker, i am posting my question here.
I am currently in the process of analyzing a growth model in pigs. Due to
the confidentiality of the data, I cannot add any data which is of course
the preferred course, but I hope to gain some insight here. I apologize in
advance if the description is unclear.
The data shows growth in 300+ pigs over 168 days, measured on 11
time-points. These 168 days can be divided in three separate phases:
farrowing/mom (2 timepoints), nursery (4 timepoints), and growth-finish (5
timepoints).
During each of these phases, the animals are placed in different rooms and
pens (nested in the rooms), which by definition are random factors. Also,
there is a genetic dependency of pigs (litter) nested in moms, which would
be a crossed effect, since the effect takes place across the entire
dataset, separate from the room/pen (pigs are separated from the litter
after the farrowing/mom phase).
As such, from my point of view, the room/pen are now time-varying random
effects. Since I wish to model the entire growth curve, I was wondering if
anybody knows how to incorporate time-varying random effects?
My gut feeling tells me this is quite easy, but my models do not converge.
If you need more information, please let me know.
Marc
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