Matvec Users’ Guide

11.2. PENALIZED QUASI-LIKELIHOOD 81
M.weight("n");
M.variance("rep",2);
M.variance("rep*logdose",.1);
M.variance("rep","rep*logdose",.01);
M.link("logit",0)
M.fitdata(D);
M.glim(10);
M.vce_aireml(25,0);
M.contrast("formul",[1 ,-1]);
M.info("dose_rr.info")
The resulting dose rr.info:
some extra information in the model
--------------------------------------------------------
AI REML converged
maximum log restricted likelihood = -295.803
variance for rep =
0.892352 -0.0532517
-0.0532517 0.029527
residual variance =
1
MME dimension : 16
non-zeros in MME: 92
basic statistics for dependent variables
-----------------------------------------------------
trait-name n mean std
perc_pos 70 0.371429 0.348139
--------------------------------------------------------
11.2.2 Multivariate Correlated Random Effects
Data on body weight at 6 weeks of age and adjusted 4-6 week feed intake on 284 mice 3 will be used to illustrate
a multi-trait analysis with correlated random effects. The model includes fixed effects for generation, litter
size, and sex. The random effects include direct and maternal genetic effects and a litter effect. The structures
of the covariance matrices are
⎛
⎞
σa1 2 σ a12 σ a1m1 σ a1m2 ( G = ⎜ σ a1a2 σa2 2 σ a2m1 σ a2m2
⎟ σ
⎝σ a1m1 σ a2m1 σm1 2 σ m12
⎠ , C = 2
c1
σ c12
σa1m2 2 σ a1m2 σ m12 σm2
2
Using the following variance component estimates,
σ c12
σc2
2
)
, and R =
⎛
⎞
4.2 −.08 .41 −.12 ( )
G = ⎜−.08 5.9 1.4 −.65
⎟ .25 .24
⎝ .41 1.4 1.7 −1.7⎠ , C = , and R =
.24 1.8
−.12 −.65 −1.7 2.1
BLUEs and BLUPs can be obtained with the following code:
( σ
2
e1 σ e12
σ e12
σ 2 e2
( )
2.1 2.5
,
2.5 1.8
3 Meyer, K. (1991). Estimating variances and covariances for multivariate Animal Models by Restricted Maximum Likelihood.
Genet. Sel. Evol. 23: 67-83.
)
.