Description Usage Arguments Author(s)
Sparse group lasso cross validation using multiple possessors
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)
|
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 |
a list of indices of a cross validation
splitting. If |
max.threads |
the maximal number of threads to be used |
seed |
the seed used for generating the random cross
validation splitting, only used if
|
algorithm.config |
the algorithm configuration to be used. |
Martin Vincent
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