Matvec Users’ Guide

64 CHAPTER 10. LINEAR MODEL ANALYSES
M.fitdata(D);
M.blup();
M.save("mme.out");
10.4 Linear Estimation
M.estimate(Kp) returns BLUE(K’b), M.covmat(Kp) returns Var(K’ˆb-K’b), and M.label(i) returns the label
of model effect i where Kp means K’. For example, the following Matvec script is saved in a file called try3.
P = Pedigree();
P.input("try.ped");
D = Data();
D.input("try.dat","animal\$ herd _skip y");
M = Model();
M.equation("y = intercept herd animal");
M.variance("residual",2);
M.variance("animal",P,1);
M.fitdata(D);
Kp=[1 1 0
1 0 1
0 1 ,-1];
herd=M.estimate(Kp);
var=M.covmat(Kp);
for(i=1;i input try3
Effect 1 intercept:1
Effect 2 herd:1
Effect 3 herd:2
Herd 1 7.42 +- 1.19861
Herd 2 9.11 +- 1.12213
Herd 1 - 2 -1.69 +- 1.3997
10.5 Linear Hypothesis Test
M.contrast(Kp,m) is doing a linear hypothesis test that H0: K’b = m. Vector m is optional, the default is
a vector of 0’s. For example, the following Matvec script is saved in a file called try4.
P = Pedigree();
P.input("try.ped");