Matvec Users’ Guide

66 CHAPTER 10. LINEAR MODEL ANALYSES
Now, at the Matvec prompt, I type
> input try5
LEAST SQUARE MEANS
-------------------------------------------------------------
herd lsmean sqrt(Var(kb)) p_value estimability
1 7.42 1.4594 0.00705964
2 9.11 1.36628 0.00262883
-------------------------------------------------------------
note 1: estimability = Max(mme*ginv(mme)*k - k);
if it is non-zero, lsmean may or may not be estimable.
note 2: sqrt(Var(kb)) is also called STD ERR (see SAS)
10.7 Variance Components Estimation (VCE)
There are two member functions of a model object for variance components estimation: M.vce emreml() and
M.vce dfreml().
M.vce_emreml(maxiter,interactive,stopval) estimates the variance components for random effects
using EM REML algorithm. Three arguments are optional. The first argument maxiter is the maximum
number of iterations allowed with the default value 1000; if you are running Matvec interactively, it allows you
to add more iterations at run-time, thus a recommended maxiter value is 20 in order to monitor increment
of restricted log-likelihood. The second argument interactive is the switch for interactive mode or noninteractive
with the default value 1. The third argument stopval is the stop criterion with the default value
1.0e-5.
M.vce_dfreml("df_method",maxiter,stopval) estimates the variance components for random effects
using DF REML algorithm. Three arguments are optional. The first argument "" is the
name of derivative-free based maximization maximum algorithm with default name "powell"; the second
maxiter is the maximum number of iterations allowed with default value 500000; the third stopval is the
stop criterion with default value 1.0e-5.
Both member functions return the BLUP solution which used the latest estimated variance components.
Therefore, a semicolon (;) should always be used to end the functions to suppress printing of the length
BLUP solution.
For example, the following Matvec script is saved in a file called try6.
D = Data();
D.input("try.dat","animal\$ herd _skip y");
P = Pedigree();
P.input("try.ped");
M = Model();
M.equation("y = intercept + herd + animal");
M.variance("residual",2.0); // initial variance value
M.variance("animal",P,1.0); // initial variance value
M.fitdata(D);
M.vce_emreml(); // correct answer: sigma_e = 2.69863,
// sigma_a = 2.20751
Now, at the Matvec prompt, I type
> input try6
iteration sigma_1 ..... sigma_e log_likelihood
0: 1 2 -6.10457331