It was assumed that g ~ N 0, G σ g 2 and e ~ N 0, D σ e 2, where G is the genomic relationship matrix, σ g 2 is the additive genetic variance, D is a diagonal matrix with weights of the residual variance [ 15], and σ e 2 is the residual variance.
Therefore, Π p in [9] is a diagonal matrix with weighting factors in each of its diagonal elements.
From this, the weighting matrix R is set as an identity matrix with weight states for all inputs, and Q matrix must be given.
There is the possibility of conducting CCO studies using an ecologic matrix, with weighting for the daily number of events in the regression models.
Since we need quantitative relationships in the networks in order to generate gene-expression data from each network, in the data generation process, we use adjacency matrices with signed weights.
A weighting matrix with finite weights is applied for the control of complex modes, whereas a weighting matrix with an infinite weight is used for controlling the divergent mode, with roots being real.
The corresponding adjacency matrix W with weights w ij represents the "strength" of the relationship between vertices i, j ∈ V; V M ⊂ V denotes a subset of "positive" vertices belonging to a specific MeSH subject heading M (e.g. a MeSH descriptor of a disease – Section 2.1).
The NLO-active ASD containing nitroazobenzene was incorporated into the melamine matrices with different weight ratios.
Thus, the PLSR coefficients can be estimated by β ^ = L L T Z T ZL − 1 H T, where L and H denote matrices with the weight vectors l 1 and h 1 as their columns.
where M and W zm are the Jacobian and the weighting sub-matrix (with large weights) of the mesh constraints, respectively.
