Hi everyone,
I have a mixed model experiment that involves some repeated measurements I'm trying to find the appropriate code. I have a split-plot experiment involving 54 rows of lettuce. I have 3 light supplemental light treatments with 18 rows each. Within each light treatment I had two nitrogen treatments by 3 lettuce varieties. So 9 rows got 1 nitrogen treatment and another 9 got the other nitrogen treatment. Each nitrogen treatment had 3 varieties of lettuce and I had 3 replications. There was 3 rows with the same lettuce variety in each nitrogen treatment.
Every 7 days I took instantaneous gas exchange measurements to measure photosynthesis rate, transpiration rate, stomachs conductance, and chlorophyll content. I took 1 measurement on 1 plant per row each week for 5 weeks. I'm treating each row as an experimental unit.
I would like analyze these measurements as repeated measurements to see how the light and nitrogen treatments affect them over time. I'm new to repeated measurements so I've been reading SAS for Mixed Models by Littel and Milliken et al. 2006.
proc mixed data = cycle1.fallphysiology;
CLASS Rep Light Ncon Var DAT;
MODEL A = Light | Ncon | Var | DAT/DDFM=Satterthwaite;
random Rep(Light) Ncon*Var*Rep(Light);
Repeated DAT / subject=Ncon*Var*Rep(Light) type=vc r rcorr;
RUN;
Rep = replication
Ncon = nitrogen concentration
Var = variety
DAT = days after transplanting or every 7 days
When I run this code however almost all of the effects are significant which both my advisor and I find suspicious.
So I'm trying to figure out if this model is appropriate. I believe I have the right subject since this should be the same row in each week. I've kept the covariance as independent because I measured a different plant each week. So from my understanding these are not technically repeated measurements and there shouldn't be much coloration between weeks. However, I'd still like to treat them as repeated measurement if it makes sense to do so. I think I may also need a random statement. I've seen some examples use and others not
I've included one of my datasets along with the log and output of this code.
Any advice on if this model is appropriate for this experiment would be greatly appreciated.
Thanks!
