Good day SAS friends:
I have this data set:
data have;
input SIRE DAM WEIGHT IA_NUMBER;
WEIGHTxIA_NUMBER = WEIGHT*IA_NUMBER;
cards;
1 1 35 2
1 2 47 1
1 3 60 1
1 4 51 1
1 5 65 2
1 6 77 3
2 7 40 1
2 8 51 5
2 9 54 4
2 10 63 2
2 11 70 6
3 12 37 1
3 13 37 1
3 14 49 1
3 15 53 5
;
proc GLM data = have;
class SIRE DAM;
model WEIGHT IA_NUMBER = SIRE;
run;
This data set can be find in this book:
http://www.livrosabertos.sibi.usp.br/portaldelivrosUSP/catalog/view/162/147/713-1
The first two ANOVAS are easy to find, the problem is when the autor call the products of WEIGHT and IA_NUMBER, wich Analisys of CO-variance between WEIGHT and IA_NUMBER would be:
Variance Font | Freedom degrees | Product Sumatory | Medium Product |
Sire | 2 | 25.5 | 12.75 |
Residual | 12 | 114.9 | 9.58 |
Total | 14 | 140.4 |
This result appears in page 194 of the same book.
Thanks in advance for the tip.
Regards
Hi,
I'm not sure that I follow the question and I'm definitely not a statistician, but I can tell you that PROC GLM supports cross effects syntax like this:
proc GLM data = have;
class SIRE DAM;
model SIRE= WEIGHT IA_NUMBER WEIGHT*IA_NUMBER;
run;
Maybe that helps? If not, please post back with a more specific question and details of what you tried.
thanks for the answer, but it is not correct.
Sire and Dam are dependent variables, mean while weight ant ia_number are independent.
@jonatan_velarde - right, sorry about that. I was just showing the cross effect syntax, not trying to solve (because that's above my skill).
Do you use SAS Studio (or SAS University Edition) or SAS Enterprise Guide? Each of these has a Linear Models task that can generate the PROC GLM code for you, and it might be easier to locate the options you need. You can learn the syntax by allowing the task to help you with your choices.
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