Matvec Users’ Guide

10.3. BEST LINEAR UNBIASED PREDICTION (BLUP) 61
XPy = X’*y;
ZPy = Z’*y;
rhs = [XPy;
ZPy];
sigma_e = 2.0;
sigma_a = 1.0;
r = sigma_e/sigma_a;
G = A*sigma_a;
MME = [XPX, XPZ;
XPZ’, ZPZ + A.inv()*r];
blup = MME.ginv()*rhs
Now, at the Matvec prompt, simply type
> input try1
blup =
Col 1
Row 1 5.51000
Row 2 1.91000
Row 3 3.60000
Row 4 0.260000
Row 5 -0.260000
Row 6 0.260000
Row 7 -0.670000
Row 8 0.670000
The above script written in Matvec language is the exactly what Professor Gianola told me to do when I
was taking his linear model course. Step by step, I have clearly shown you what I was doing in the above
script. Now I will do the same job in an encapsulated but efficient way using Matvec higher level functions.
10.3.4 Obtain BLUP using Matvec higher level functions
M.blup() returns the blup as a vector if M is a Model object. I wrote down the following Matvec script and
saved it in an ASCII file called try2:
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);
M.blup()
There are two Matvec features I want to mention here: