Generic sparse group lasso subsampling procedure

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Description

Support the use of multiple processors.

Usage

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sgl_subsampling(module_name, PACKAGE, data, parameterGrouping, groupWeights,
  parameterWeights, alpha, lambda, training, test, collapse = FALSE,
  max.threads = NULL, use_parallel = FALSE,
  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, a vector or a list of vectors (of the same length) with the lambda sequence for the subsamples.

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

Deprecated (will be removed in 2018), instead use use_parallel = TRUE and registre parallel backend (see package 'doParallel'). The maximal number of threads to be used.

use_parallel

If TRUE the foreach loop will use %dopar%. The user must registre the parallel backend.

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 sequences used (a vector or list of length length(training)).

Author(s)

Martin Vincent

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