sgl_subsampling: Generic sparse group lasso subsampling procedure
In sglOptim: Generic Sparse Group Lasso Solver

Description Usage Arguments Details Value Author(s)

Subsampling procedure with support parallel computations.

sgl_subsampling(module_name, PACKAGE, data, parameterGrouping = NULL,
  groupWeights = NULL, parameterWeights = NULL, alpha, lambda,
  d = 100, compute_lambda = length(lambda) == 1, training = NULL,
  test = NULL, responses = NULL, auto_response_names = TRUE,
  collapse = FALSE, max.threads = NULL, use_parallel = FALSE,
  algorithm.config = sgl.standard.config)

`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`	lambda.min relative to lambda.max (if `compute_lambda = TRUE`) or 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.
`d`	length of lambda sequence (ignored if `compute_lambda = FALSE`)
`compute_lambda`	should the lambda sequence be computed
`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.
`responses`	a vector of responses to simplify and return (if NULL (deafult) no formating will be done)
`auto_response_names`	set response names
`collapse`	if `TRUE` the results will be collapsed and ordered into one result, resembling the output of `sgl_predict` (this is only valid if the test samples 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.

If no formating is done (i.e. if responses = NULL) then the responses field contains a list of lists structured in the following way:

subsamples 1:

sample test[[1]][1]
- model (lambda) index 1
  - response elements
- model (lambda) index 2
  - response elements
- ...
sample test[[1]][2]
- model (lambda) index 1
  - response elements
- model (lambda) index 2
  - response elements
- ...
...

subsamples 2: ...

If responses = "rname" with rname the name of the response then a list at responses$rname will be returned. The content of the list will depend on the type of the response.

vector A list with format subsamples -> models -> matrix of dimension n_i \times q containing the responses for the corresponding model and subsample (where q is the dimension of the response).
matrix A list with format subsamples -> samples -> models - > the response matrix.

`Y.true`	the response, that is the `y` object in data as created by `create.sgldata`.
`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)`).