Usage
sgl_subsampling(module_name, PACKAGE, data,
parameterGrouping, groupWeights, parameterWeights,
alpha, lambda, training, test, collapse = FALSE,
max.threads = 2,
algorithm.config = sgl.standard.config)Arguments
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 $\beta$.
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 $\alpha$ 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.
training
a list of training samples, each item of
the list corresponding to a subsample. Each item in the
list must be a vector with the indices of the training
samples for the corresponding subsample. The length of
the list must equal the length of the
test
a list of test samples, each item of the list
corresponding to a subsample. Each item in the list must
be vector with the indices of the test samples for the
corresponding subsample. The length of the list must
equal the length of the traini
collapse
if TRUE the results for each
subsample will be collapse into one result (this is
useful if the subsamples are not overlapping)
max.threads
the maximal number of threads to be
used.
algorithm.config
the algorithm configuration to be
used.