```Date: Thu, 29 Jul 1999 15:43:06 -0400 Reply-To: "Hamani, Elmaache" Sender: "SAS(r) Discussion" From: "Hamani, Elmaache" Subject: SAS-Biostatician to help Content-Type: multipart/alternative; Hi guys. I run a program on the softwear MLwiN and I have the results. I would like to try it on SAS. But I can. I will try to explain you as follows the problem: I am trying to write the hierarchical model ( 2 levels j is the level 2=Family and i is level 1=twin), with some constrains. My genetic equation is Yij= m + Cj +R_1j* MZj + R_2j*DZj + R_2ij(DZ) + e_ij where Yij response and: Cj , R_1j, R_2j are randon variables of level 2 (or elements of G in PROC MIXED) and ( R_2ij ) and ( e_ij ) are randon variables of level 1 (or elements of R in PROC MIXED) The two constraints are: Var(R_1j)=2*Var(R_2j) and Var(R_2j)=Var(R_2ij(DZ) . By using PROC MIXED with LDATA and LIN(q), I solved the first constrain and I estimed Var(Cj ), but I can not resolve the second constrain, that is Var(R_1j)=Var(R_2ij(DZ). The following is a part of the input: Family twin sexe Y MZ DZ 1 1 0 13 0 1 1 2 1 16 0 1 2 1 0 17 1 0 2 2 0 17 1 0 3 1 0 16 1 0 3 2 0 17 1 0 4 1 0 14 0 1 4 2 0 17 0 1 5 1 0 12 1 0 5 2 0 14 1 0 6 2 0 13 0 1 6 1 1 13 0 1 7 1 1 22 0 1 7 2 1 13 0 1 ; /* here my program */ data con; input parm row col1-col3; datalines; 1 1 1 0 0 2 2 0 2 0 2 3 0 0 1 ; run; PROC MIXED data=un noclprint covtest ; class family ; model nmis_agr = / noint; random int dz mz /type=lin(2) ldata=con sub=famille G ; run; /*************************************/ A part of ouput: G Matrix Effect FAMILY Row COL1 COL2 COL3 INTERCEPT 1001 1 0.26700391 DZ 1001 2 0.09326706 MZ 1001 3 0.18653412 Covariance Parameter Estimates (REML) Cov Parm Subject Estimate StdError Z Pr > |Z| LIN(1) FAMILLE 0.26700391 0.15804857 1.69 0.0911 LIN(2) FAMILLE 0.09326706 0.11151294 0.84 0.4029 Residual 0.53656972 0.04241956 12.65 0.0001 It misses here DIAG (DZ) =? for the seconde relationship:Var(R_2j)=Var(R_2ij(DZ) ). Thanks a lot. best regards. [text/html] ```

Back to: Top of message | Previous page | Main SAS-L page