s2Params

elastic-net regularization parameter - \(l_1\) norm.

lambda1

elastic-net regularization parameter - \(l_2\) norm.

lambda2

s2net weight hyper-parameter.

gamma1

s2net covariance hyper-parameter (between 1 and <code>Inf</code>).

gamma2

s2net shift hyper-parameter (between 0 and 1).

gamma3

This is a very simple function that collapses the input parameters into a named vector to supply to C++ methods.

Implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models. We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.

s2Params: Hyper-parameter wrapper for `s2net`

Description

Usage

Arguments

Value

See Also