sgl_subsampling: Generic sparse group lasso subsampling procedure

In sglOptim: Sparse group lasso generic optimizer

Description

Support the use of multiple processors.

Usage

sgl_subsampling(module_name, PACKAGE, data, parameterGrouping, groupWeights,
  parameterWeights, alpha, lambda, training, test, collapse = FALSE,
  max.threads = 2, algorithm.config = sgl.standard.config)

Arguments

module_name	reference to objective specific C++ routines.
PACKAGE	name of the calling package.
data	a list of data objects – will be parsed to the specified module.
parameterGrouping	grouping of parameters, a vector of length p. Each element of the vector specifying the group of the parameters in the corresponding column of β.
groupWeights	the group weights, a vector of length length(unique(parameterGrouping)) (the number of groups).
parameterWeights	a matrix of size q \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.
training	a list of training samples, each item of the list corresponding to a subsample. Each item in the list must be a vector with the indices of the training samples for the corresponding subsample. The length of the list must equal the length of the test list.
test	a list of test samples, each item of the list corresponding to a subsample. Each item in the list must be vector with the indices of the test samples for the corresponding subsample. The length of the list must equal the length of the training list.
collapse	if TRUE the results for each subsample will be collapse into one result (this is useful if the subsamples are not overlapping)
max.threads	the maximal number of threads to be used.
algorithm.config	the algorithm configuration to be used.

Value

responses	content will depend on the C++ response class
features	number of features used in the models
parameters	number of parameters used in the models
lambda	the lambda sequence used.

Author(s)

Martin Vincent

sglOptim documentation built on May 2, 2019, 5:55 p.m.