sgl_cv: Generic sparse group lasso cross validation using multiple...
In sglOptim: Sparse group lasso generic optimizer

Description Usage Arguments Value Author(s)

Generic sparse group lasso cross validation using multiple possessors

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sgl_cv(module_name, PACKAGE, data, parameterGrouping, groupWeights,
  parameterWeights, alpha, lambda, fold = 2, cv.indices = list(),
  max.threads = 2, 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`	the lambda sequence for the regularization path.
`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.
`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.
`max.threads`	the maximal number of threads to be used.
`algorithm.config`	the algorithm configuration to be used.

`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.