sgl_cv: Sparse group lasso cross validation using multiple possessors In SglOptimizer: Sparse group lasso optimizer

Description

Sparse group lasso cross validation using multiple possessors

Usage

 1 2 3 4  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 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 seed the seed used for generating the random cross validation splitting, only used if fold≤max(table(classes)). algorithm.config the algorithm configuration to be used.

Author(s)

Martin Vincent

SglOptimizer documentation built on May 31, 2017, 3:04 a.m.