Table 1 Parameters u and \(\alpha\) for the penalised complexity priors of hyper-parameters by fitted models to the simulated and real data (see "Prior distributions for hyper-parameters" section)
From: Spatial modelling improves genetic evaluation in smallholder breeding programs
Model | \(u_e,\, \alpha _e\) | \(u_a, \, \alpha _a\) | \(u_h, \, \alpha _h\) | \(u_{s}, \, \alpha _{s}\) | \({u_{\rho }, \, \alpha _{\rho }}^a\) | \({u_{\rho }, \, \alpha _{\rho } }^b\) |
|---|---|---|---|---|---|---|
G | 0.30, 0.50 | 0.10, 0.50 | – | – | – | – |
GH | 0.15, 0.50 | 0.10, 0.50 | 0.25, 0.50 | – | – | – |
GS | 0.15, 0.50 | 0.10, 0.50 | – | 0.25, 0.50 | 0.60, 0.95 | 50, 0.80 |
GHS | 0.15, 0.50 | 0.10, 0.50 | 0.15, 0.50 | 0.10, 0.50 | 0.60, 0.95 | 50, 0.80 |