Distance | PQc (R2d) | RMSEa
| Optimal LVsb
|
---|
Euclidean | 0.62 ± 0.005 | 370 ± 2.7 | 4 |
Gower | 0.60 ± 0.0052 | 380 ± 2.8 | 6 |
Allele share | 0.61 ± 0.005 | 380 ± 2.8 | 6 |
Nei | 0.59 ± 0.005 | 390 ± 2.9 | 6 |
Bray | 0.63 ± 0.004 | 370 ± 2.6 | 4 |
Jaccard | 0.64 ± 0.0043 | 360 ± 2.8 | 4 |
Kulczynski | 0.61 ± 0.0053 | 380 ± 2.8 | 4 |
GRM | 0.62 ± 0.005 | 370 ± 2.9 | 5 |
GBLUP | 0.61 ± 0.001 | 369.9 ± 0.66 | NA |
- All the results presented in table are significant (with respect to p-value computed from permutation analysis). The results are averaged over 10-fold CV scheme. The 10-fold CV procedure was repeated 50 times. The standard error (se) calculated over 10-fold CV repetition. The last row present prediction results obtained from GBLUP. The PQ (R2d), RMSE and LVs represents prediction quality, root mean square error and latent variables respectively
-
a
RMSE stands for root mean square error
-
b
LVs stands for latent variables used for model building
-
c
PQ represent prediction quality
-
d
R
2 presented in the table are estimated for testset and not from training model. The value is calculated in a cross validation setup (some time indicated as Q2). This value is refer as prediction quality in this study