Matvec Users’ Guide

82 CHAPTER 11. GENERALIZED LINEAR MIXED MODEL ANALYSES
D=Data();
D.input("mouse/mouse_2.dat",\
" animal sire dam byr sex littersize siregroup _skip wt_6wk"+\
" fd_int directpe dampe");
P=Pedigree();
P.input("mouse/mouse.ped","individual father mother");
M=Model();
M.equation("wt_6wk=intercept byr sex littersize animal dam dampe,"+\
"fd_int = intercept byr sex littersize animal dam dampe");
R=[2.1 2.5
2.5 13];
GD=[4.2, -.08
-.08 5.9];
GM=[1.7 , -1.7
-1.7 2.1];
GDM=[.41 ,-.12
1.4,-.65];
C=[.25 .24
.24 1.8];
M.variance("residual",R)
M.variance("animal",P,GD)
M.variance("dam",P,GM)
M.variance("animal","dam",GDM)
M.variance("dampe",C)
M.fitdata(D);
M.blup();
M.save("mouse3a.out","out");
A portion of mouse3a.out:
some extra information in the model
--------------------------------------------------------
variance for animal =
4.20000 -0.0800000 0.410000 -0.120000
-0.0800000 5.90000 1.40000 -0.650000
0.410000 1.40000 1.70000 -1.70000
-0.120000 -0.650000 -1.70000 2.10000
variance for dampe =
0.250000 0.240000
0.240000 1.80000
residual variance =
2.10000 2.50000
2.50000 13.0000
MME dimension : 1466
non-zeros in MME: 22059
. . . . . . . .
Term Trait 1 Trait 2
animal wt_6wk fd_int
13c 0.528968 0.473886
13r -0.267458 0.581903
16b -0.267458 0.581903
. . . . . . . .
Term Trait 1 Trait 2