A matrix, similar to this was used in Gianola et al. (2011) for predicting 
milk, fat and protein production in Jersey cows. In this software version we do not center the incidence 
matrix for the additive effects.
\(G=\frac{X_a X_a'}{2\sum_{j=1}^p p_j (1-p_j)},\)
where
<ul>
<li>\(X_a\) is the design matrix for allele substitution effects for additivity.</li>
<li>\(p_j\) is the frecuency of the second allele at locus \(j\) and \(q_j=1-p_j\).</li>
</ul>

datasets

Bayesian regularization for feed-forward neural networks.

Paulino Rodriguez

brnn

Bayesian Regularization for Feed-Forward Neural Networks

G function

A matrix, similar to this was used in Gianola et al. (2011) for predicting 
milk, fat and protein production in Jersey cows. In this software version we do not center the incidence 
matrix for the additive effects.
\(G=\frac{X_a X_a'}{2\sum_{j=1}^p p_j (1-p_j)},\)
where
<ul>
<li>\(X_a\) is the design matrix for allele substitution effects for additivity.</li>
<li>\(p_j\) is the frecuency of the second allele at locus \(j\) and \(q_j=1-p_j\).</li>
</ul>

G: Genomic additive relationship matrix for the Jersey dataset.

Description

Arguments

References