Sparse group lasso cross validation using multiple possessors

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Description

Sparse group lasso cross validation using multiple possessors

Usage

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  sgl_cv(module_name, PACKAGE, data, covariateGrouping,
    groupWeights, parameterWeights, alpha, lambda,
    fold = 2L, cv.indices = list(), max.threads = 2L,
    seed = 331L, algorithm.config = sgl.standard.config)

Arguments

call_sym

reference to objective specific C++ routines

data
covariateGrouping

grouping of covariates, a vector of length p. Each element of the vector specifying the group of the covariate.

groupWeights

the group weights, a vector of length m+1 (the number of groups).

parameterWeights

a matrix of size K \times (p+1).

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 foldmax(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

seed

the seed used for generating the random cross validation splitting, only used if foldmax(table(classes)).

algorithm.config

the algorithm configuration to be used.

Author(s)

Martin Vincent