sgl_fit: Fit a sparse group lasso regularization path.

In SglOptimizer: Sparse group lasso optimizer

Description

Fit a sparse group lasso regularization path.

Usage

  sgl_fit(module_name, PACKAGE, data, covariateGrouping,
    groupWeights, parameterWeights, alpha, lambda,
    return = 1:length(lambda),
    algorithm.config = sgl.standard.config)

Arguments

call_sym	reference to objective specific C++ routines
data
covariateGrouping	grouping of covariates, a vector of length p. Each element of the vector specifying the group of the covariate.
groupWeights	the group weights, a vector of length m (the number of groups).
parameterWeights	a matrix of size K \times (p).
alpha	the α value 0 for group lasso, 1 for lasso, between 0 and 1 gives a sparse group lasso penalty.
lambda	the lambda sequence for the regularization path.
return	the indices of lambda values for which to return a the fitted parameters.
algorithm.config	the algorithm configuration to be used.

Value

beta	the fitted parameters – a list of length length(lambda) with each entry a matrix of size K\times (p+1) holding the fitted parameters
loss	the values of the loss function
objective	the values of the objective function (i.e. loss + penalty)
lambda	the lambda values used

Author(s)

Martin Vincent

SglOptimizer documentation built on May 2, 2019, 5:17 p.m.