sgl_cv: Generic sparse group lasso cross validation using multiple...
In sglOptim: Generic Sparse Group Lasso Solver

Description Usage Arguments Value Author(s)

Generic sparse group lasso cross validation using multiple possessors

sgl_cv(module_name, PACKAGE, data, parameterGrouping = NULL,
  groupWeights = NULL, parameterWeights = NULL, alpha, lambda,
  d = 100, compute_lambda = length(lambda) == 1, fold = 2,
  sampleGroups = NULL, cv.indices = list(), responses = NULL,
  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
`fold`	the fold of the cross validation, an integer larger than 1 and less than N+1. Ignored if `cv.indices != NULL`. If `fold`≤`max(table(classes))` then the data will be split into `fold` disjoint subsets keeping the ration of classes approximately equal. Otherwise the data will be split into `fold` disjoint subsets without keeping the ration fixed.
`sampleGroups`	grouping of samples, the algorithm computing the cv.indices will try to equally divide the groups among the subsamples.
`cv.indices`	a list of indices of a cross validation splitting. If `cv.indices = NULL` then a random splitting will be generated using the `fold` argument.
`responses`	a vector of responses to simplify and return (if NULL (deafult) no formating will be done)
`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.

`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
`cv.indices`	the cross validation splitting used
`features`	number of features used in the models
`parameters`	number of parameters used in the models
`lambda`	the lambda sequence used.