Hello,
I have a dataset and a set of Apriori models and I am going to use model selection and AIC to rank the models. My models have fixed and random effects. I have two random class variables, year and unit, and a suite of continuous variables. Below is a simplified sample dataset. One thing I have to consider is that some, but not all experimental units were sampled each year.
From research with SAS so far, I have found that the default estimator used in proc mixed is REML, and that REML only considers the random effects. Since the formula that calculates each AIC value includes a bias correction term based on the number of parameters, it seems that the REML method would be inappropriate for models including fixed effects. In order to consider the fixed effects, I need to specify the ML method. I have found that the ML method counts each unique observation in a class variable as a separate parameter. For example each year is counted as a separate parameter in the model. This would seem to inflate the bias correction term for AIC, as it uses the number of parameters for the calculation. I would welcome any suggestions on the best way to proceed with this analysis. I am wondering whether or not SAS is the best environment to perform model selection, and I plan on calculating AIC values manually as a check. Any recommendations or insight on how best to proceed with this analysis are welcome.
Thanks
y year unit x3 x4 x5 x6
43 2005 A 23 37 19 7
34 2005 B 14 48 28 31
50 2005 C 19 24 48 48
4 2005 D 47 9 46 20
28 2005 E 37 36 6 12
7 2005 F 9 27 22 19
40 2005 G 31 9 15 32
45 2006 A 17 4 29 6
24 2006 C 29 23 7 38
37 2006 D 9 26 34 32
18 2006 F 11 45 50 18
18 2006 G 27 10 16 42
17 2007 B 6 34 7 29
49 2007 C 14 2 17 26
27 2007 D 12 13 31 46
18 2007 E 4 22 46 44
28 2007 F 50 45 5 16
5 2007 G 47 23 16 16
22 2007 H 29 5 29 36
40 2007 I 9 45 15 32