We implemented ridge regression best linear unbiased prediction (RR-BLUP) of the hybrid performances considering additive and dominance effects (Zhao et al. 2014).
For genome-wide prediction, we implemented ridge regression best linear unbiased prediction (RR-BLUP), modeling both additive and dominance effects as: Y={1}_nmu +{Z}_A a+{Z}_D d+ e. Y refers to BLUEs of the hybrids for grain yield.
We used LR-TRIRLS (15), which implements ridge regression, to build LR models.
To demonstrate the disease discriminatory capacity of Col5a2, a classification model based on logistic regression was implemented (Ridge estimation value: 1E-08).
Meuwissen et al. (2001) implemented ridge regression in GS by assuming that the marker effects (m j′s j = 1… p) were random, and they were drawn from a normal distribution with V a r (m j ) = σ m 2, where σ m 2 = σ a 2 / n k σ a 2 represents additive genetic variance expressed among individuals and n k is the number of marker loci (Habier et al. 2007).
In order to circumvent the further effects of variable missingness or strong correlations among our variables, as well as the effects of the number of observations (ie, 5,444 individuals) being higher than our number of parameters (ie, 2,058 questions), we implemented a ridge prior of at least 10% of our number of observations.
Ridge implements the automatic choice of ridge parameter presented in this paper, and is freely available from CRAN.
To eliminate noise effect, a penalty function is imposed first, and then the dynamic optimization technique is implemented for wavelet ridge extraction.
This methodology was chosen to reduce overfitting by penalizing "model complexity" implemented in the ridge regression.
Next, these processes are implemented: 1. Ridge pixels are thresholded using two thresholds T1 and T2 with T1⟨ T1⟨
There are extensions of this work on similar soils in Ethiopia (ILCA 1985) with an interesting development of a very simple ridging implement shown in Plate 5.4.
