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In the derivation data set, an inverse-variance weighted multivariate linear regression was performed to provide derivation data set coefficients (β ± SE) for each term in the models.
The elastic net, an automatic method of variable selection, interpolates between L1- (LASSO) and L2- (ridge) regularization and can effectively shrink coefficients and set some coefficients to zero.
In the first set, coefficients in the volume equation were refitted for each pulse density/plot size combination.
In the second set, coefficients in the volume equation were fixed at those used for the highest plot size (0.06 ha) and pulse density (4 pulses m-2).
We set dominance coefficient h = 0.5 and selection coefficient s to either 0.05 or 0.01.
For SAMtools, we set homopolymer coefficient h = 50.
Using the absolute values as condition yields shrinkage in some coefficients and simultaneously may set other coefficients to zero as has been shown by Tibshirani [ 11].
When the current setting coefficient is over 1.8, both connectivity level and global effective performance reach their maximum values.
Ridge regression forces all the coefficients to shrink toward zero equally, while LASSO can set several coefficients that are unrelated to the phenotype to zero.
By definition, the content-blind models use the same set of coefficients for all contents while the content-aware models have one set of coefficients per content (see Tables 4 and 5).
set the coefficients of the to rows of to.
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