title: Numerical examples from Mrode (2014) author: Yutaka Masuda date: September 2019 subject: "Introduction to BLUPF90 suite programs" tags: [introduction,tutorial] ...
Here we apply the same model described in the previous example to the data with missing observations. Model descriptions and mixed model equations are identical as before.
With a missing observation, $\mathbf{R}_0$ and its inverse should be altered. For example, assuming a 2
trait model, if the observation of the first trait is missing, the first row and column in $\mathbf{R}_0$ should be
zeroed out. The corresponding inverse is the generalized inverse of this altered $\mathbf{R}0$. Illustrating this
situation with the previous example, the result is
$$
\mathbf{R}{0}
\left[ \begin{array}{rr} 0&0\ 0&1/30 \end{array} \right]. $$
The generalized inverse of this zeroed matrix is equivalent to the inverse of a matrix containing
only nonzero elements in the zeroed matrix (Searle, 1971).
BLUPF90 can detect a missing observation and prepares an appropriate
One animal is added to the previous example and 2 observations are marked as missing.
The missing observation is indicated as 0, which is the default missing code used in the BLUPF90 family
(data_mr05b.txt).
We can use an extended pedigree file as the previous one by adding the animal 9 (pedigree_mr05b.txt).
The parameter file is also identical except for omitting an option for standard error calculations.
DATAFILE
data_mr05b.txt
NUMBER_OF_TRAITS
2
NUMBER_OF_EFFECTS
2
OBSERVATION(S)
5 6
WEIGHT(S)
EFFECTS:
2 2 2 cross
1 1 9 cross
RANDOM_RESIDUAL VALUES
40.0 11.0
11.0 30.0
RANDOM_GROUP
2
RANDOM_TYPE
add_animal
FILE
pedigree_mr05b.txt
(CO)VARIANCES
20.0 18.0
18.0 40.0
OPTION solv_method FSPAK
You can confirm the results are identical to the values in the textbook (p.80).