Mixed linear model for the prediction of breeding values and estimation of fixed effects under an animal model. The model is implemented and tested on data from Chapter 3 (Mrode RA, 2014).
Consider the data set in Table 3.1 for the pre-weaning gain (WWG) of beef calves (calves assumed to be reared under the same management conditions).
where: Yij = the WWG of the jth calf of the ith sex; Pi = the fixed effect of the ith sex; Aj = random effect of the jth calf; and eij = random error effect.
Example 3.1.
| Calf | Sire | Dam | Sex | WWG (Kg) |
|---|---|---|---|---|
| 4 | 1 | Unknown | Male | 4.51 |
| 5 | 3 | 2 | Female | 2.92 |
| 6 | 1 | 2 | Female | 3.93 |
| 7 | 4 | 5 | Male | 3.54 |
| 8 | 3 | 6 | Male | 5.0 |
python setup.py test
pip install -e blupper
Following command
python blup_generator.py --input_csv ./blupper/tests/test_data/eight_animals_data.csv --output_csv OUT.csv --sigma_sq_a 20 --sigma_sq_e 40 --response_var WWG
Produce this table:
| Animal | BLUP | r_squared | r | SEP |
|---|---|---|---|---|
| 1 | 0.09844457570387988 | 0.057811577082102494 | 0.2404403815545602 | 4.34094096462483 |
| 2 | -0.018770099100871906 | 0.01580855811511439 | 0.12573208864531915 | 4.436646124912118 |
| 3 | -0.04108420292708481 | 0.08708243092472268 | 0.29509732449604265 | 4.272979216133113 |
| 4 | -0.008663122661940692 | 0.14463969285292366 | 0.3803152545624796 | 4.136085848110691 |
| 5 | -0.1857320994946512 | 0.14378650653015657 | 0.37919191253263373 | 4.138148120765721 |
| 6 | 0.17687208768130214 | 0.11543446872743957 | 0.3397564844523789 | 4.206103972258794 |
| 7 | -0.24945855483363033 | 0.11628765505020666 | 0.3410097579985163 | 4.20407503489125 |
| 8 | 0.18261468793069424 | 0.15527170702894255 | 0.3940453108830792 | 4.110299971951092 |