Generic sparse group lasso subsampling procedure

Description

Support the use of multiple processors.

Usage

 1 2 3 4 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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