Problem 1: Sire Model
Use the following dataset to predict breeding values using a sire
model. The dataset is available from
https://charlotte-ngs.github.io/asmss2023/data/asm_ped_sim_data.csv 
 
Hints
- The variance component \(\sigma_s^2\) of the sire effect can be
assumed to be \(2.25\).
- The variance component \(\sigma_e^2\) of the random resiudals is
\(36\).
- Sex is modelled as a fixed effect.
- The sire pedigree relationship can be computed using the
pedigreemmpackage.
Your Solution
- Read the data 
- Compute the inverse sire relationship matrix 
- Setup the mixed model equations 
- Solve mixed model equations 
 
 
Problem 2: Animal Model
Use the same dataset as in Problem 1 to predict breeding values, but
use an animal model instead of a sire model. The dataset is available
from
https://charlotte-ngs.github.io/asmss2023/data/asm_ped_sim_data.csv 
 
Hints
- The variance component \(\sigma_u^2\) of the breeding value can be
assumed to be \(9\).
- The variance component \(\sigma_e^2\) of the random resiudals is
\(36\).
- Sex is modelled as a fixed effect.
- The numerator relationship matrix can be computed using the
pedigreemmpackage.
Your Solution
- Read the data 
- Compute the inverse sire relationship matrix 
- Setup the mixed model equations 
- Solve mixed model equations 
 
 
Problem 3: Model Comparison
Compare the order of the predicted breeding values for the sires from
the sire model and from the animal model.
Your Solution
- Compare both ordered vectors of predicted breeding values
Latest Changes: 2023-05-14 09:02:31 (pvr)
 
 
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