Good night SAS friends:
Here i come to get some help:
i have this data base:
data have; input ANIMAL SIRE trait1 trait2 trait3 WEIGH_AT_BIRTH ; cards; 2818 851 118 1 1 10.3 2819 851 118 1 1 9.8 2820 851 118 1 1 9.2 2821 851 118 1 1 10.4 2822 851 118 1 1 9.2 2823 851 118 1 1 10.9 2824 851 118 1 1 9.3 2825 851 118 1 2 10.2 2826 851 118 1 2 10.2 2827 851 118 1 2 10 2828 851 118 1 2 8.3 2829 851 118 1 2 9.2 2830 851 118 1 2 9 2831 851 118 1 2 8.5 2835 851 118 2 1 10 2836 851 118 2 1 9.1 2837 851 118 2 1 8.1 2838 851 118 2 1 9.3 2839 851 118 2 1 10 2840 852 118 2 1 9.2 2841 852 118 2 1 9.6 2842 852 118 2 1 8.2 2843 852 118 2 2 8.2 2844 852 118 2 2 10 2845 852 118 2 2 8.3 2852 852 119 1 1 9.4 2853 852 119 1 1 10.5 2854 852 119 1 1 11.3 2855 852 119 1 1 10.6 2856 852 119 1 1 9 2857 852 119 1 1 10.5 2858 852 119 1 1 10 2859 852 119 1 1 10.2 2860 852 119 1 1 10.4 2861 852 119 1 1 10.3 2862 852 119 1 1 10 2863 852 119 1 1 10.3 2864 852 119 1 2 11.2 2865 852 119 1 2 9.4 2866 852 119 1 2 10 2867 852 119 1 2 11.2 2868 852 119 1 2 11.4 2869 852 119 1 2 11 2870 852 119 1 2 11 2871 852 119 1 2 9.4 2872 852 119 1 2 12.3 2873 852 119 1 2 8.7 2877 852 119 2 1 10 2878 852 119 2 1 9.2 ;
As you know, i work modeling quantitative genetics, and the last posts i made here were associated to construct some data bases, that the're already working, now is time for modeling, aiming to choose the model that fits to the trait we are working at:
Here is the problem to solve:
Using the data base "have" i need to construct this table here:
FIXED EFFECT
TRAIT
SIRE_ESTIMATE
RESIDUAL_ESTIMATE
AIC
trait1
trait2
trait3
trait1
WEIGH_AT_BIRTH
0.2251
0.6686
125.8
0.5751
.
.
trait2
WEIGH_AT_BIRTH
0.1934
0.6994
128.2
.
0.0017
.
trait3
WEIGH_AT_BIRTH
0.1728
0.8691
138.3
.
.
0.8567
trait1 – trait2
WEIGH_AT_BIRTH
0
0.6482
121.1
0.0063
0.0497
.
trait1 – trait3
WEIGH_AT_BIRTH
0.2353
0.6815
126.7
0.0367
.
0.7583
trait2 – trait3
WEIGH_AT_BIRTH
0.2019
0.7097
128.8
.
0.0016
0.5912
trait1 – trait2 – trait3
WEIGH_AT_BIRTH
0
0.6618
122
0.0074
0.0522
0.8199
the information used to fill the table above, is obtained by ANOVA, using PROC MIXED, to test the variance for one, two and three fixed effects, manually i would have to add and erase some of them in the MODEL STATEMENT and, i came here to make it easier to obtain this information:
To explain the methodology, i will use the model that uses 2 fixed effects (trait1 and trait3) in the model,but in the table we have all possible conbinations between the fixed effects, this is the modeling part that manually would be like 2 weeks at work, and SAS can help, im sure of it.
To explain the variance associated to the trait, the trait we are studing is WEIGHT_AT_BIRTH, and the statement used to get this variance analisys is:
proc mixed data=have method=reml covtest cl; class trait1 trait2 trait3; /*Note that we are using all three fixed effects*/ model WEIGH_AT_BIRTH = trait1 trait3 /ddfm=kenwardroger; /*Here we just use the traist we want to test*/ random sire / solution; /*The random effect never changes*/ run;
This estatement offers many tables, and in those tables are the data we need to extract:
In the table called: "Covariance Parameter Estimates" we find the Estimate for sire and residual, and these numbers are used to fill the SIRE_ESTIMATE and RESIDUAL_ESTIMATE, (RED) respectivly, as you can see in the results table:
Covariance Parameter Estimates
Cov Parm
Estimate
Standard Error
Z Value
Pr > Z
Alpha
Lower
Upper
SIRE
0.2353
0.5465
0.43
0.3334
0.05
0.03019
29445057
Residual
0.6815
0.1437
4.74
0.0001
0.05
0.4688
1.0811
the Other information is the AICC (Smaller is Better) information, used to choose the best model that fits into the trait, and this value (GREEN)
Fit Statistics
-2 Res Log Likelihood
122.7
AIC (Smaller is Better)
126.7
AICC (Smaller is Better)
127
BIC (Smaller is Better)
122.7
And finishing, the significant estatistics p-value is used to know if the fixed effect is affecting the trait, this value goes to the results table (BLUE), too:
Type 3 Tests of Fixed Effects
Effect
Num DF
Den DF
F Value
Pr > F
trait1
1
9.3
5.94
0.0367
trait3
1
45.2
0.1
0.7583
as you can see, it is not the onlye result we have to introduce in the table, the model estatement shoud be changing continuously like this:
proc mixed data=have method=reml covtest cl; class trait1 trait2 trait3; (1) model WEIGH_AT_BIRTH = trait1 /ddfm=kenwardroger;
(2) model WEIGH_AT_BIRTH = trait2 /ddfm=kenwardroger;
(3) model WEIGH_AT_BIRTH = trait3 /ddfm=kenwardroger;
(4) model WEIGH_AT_BIRTH = trait1 trait2 /ddfm=kenwardroger;
(5) model WEIGH_AT_BIRTH = trait1 trait3 /ddfm=kenwardroger;
(6) model WEIGH_AT_BIRTH = trait2 trait3 /ddfm=kenwardroger;
(7) model WEIGH_AT_BIRTH = trait1 trait2 trait3 /ddfm=kenwardroger; random sire / solution; run;
I need this working, with more that 3 fixed effects, the RANDOM EFFECT will always be SIRE, because we are working in growing parameters for sheep genetic improvement.
Imagine make this manually, existing SAS taht is offering this help, i tell you this my friends because two years ago, when i was beginig to work in this field, i studied 512 models manually, now knowing those procedures of SAS programing can improve modeling with better results.
Thank you very much
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