Matvec Users’ Guide

10.3. BEST LINEAR UNBIASED PREDICTION (BLUP) 63
Term Trait 1
intercept
y
1 7.42
Term Trait 1
herd
y
1 0
2 1.69
Term Trait 1
animal
y
A1 0.26
A2 -0.26
A3 0.26
A4 -0.67
A5 0.67
Note that because of different g-inverse algorithms, the estimates of herds effects seems quite different
from the two above programs. However, the value of the estimable function h1-h2 is identical. Of course,
the BLUP values for animal additive effects are identical, too.
There are several optional arguments for M.blup() function to let user to choice appropriate solver,
maximum iterations allowed, and stop criterion. See blup entry in the reference manual for more details.
10.3.5 Multi-trait and multi-model example
P = Pedigree();
Ped.input("large.ped");
D = Data();
D.input("large.dat",
"animal\$ testdate line herd sex weight bf dg_test fce dg_farm");
M = Model("bf = weight(line) line animal",
"dg_test =
line testdate animal",
" fce = line testdate animal",
"dg_farm = herd sex line testdate animal");
M.covariate("weight");
M.variance("residual",
2.8, 10.7, 0.0182, 0.5,
10.7, 4160.0, -3.64, 1050.0,
0.0182, -3.64, 0.026, -1.3,
0.5, 1050.0, -1.3, 1840.0);
M.variance("animal",P,
1.2, 7.7, 0.003, 3.32,
7.7, 2240.0, -1.68, 470.0,
0.003, -1.68, 0.014, -0.72,
3.32, 470.0, -0.72, 410.0);