I am a dealing with a dataset which is as follows
y x1 x2 time n 0.12 1 0 0 18 0.3031 2 0 1 17 . . . . . . . . . . 1.15 3 2 23 17
outcome y (CO2, Carbon DiOxide)
- is continuous
- not independent because, at time = 0 , n = 18 (there were 18 pigs in the study). Then at time = 1, n=17 (one pig was lost, we dont know the reason why but the pig was not part the study at time =1). The Carbon emission was 0.12 at t=0, n=18, at t =1, carbon emission was 0.3031 it could be from the same 17 pigs in t=0 or a combination of old and new pigs we dont know. So for this reason I like to assume that y is not independent
- are time varying
Based on these assumptions, what modeling approach should I use if my goal is to predict y ? Any help would be much appreciated. Thanks.