Usage
sgl_cv(module_name, PACKAGE, data, parameterGrouping,
groupWeights, parameterWeights, alpha, lambda,
fold = 2, cv.indices = list(), 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.
fold
the fold of the cross validation, an integer
larger than $1$ and less than $N+1$. Ignored if
cv.indices != NULL. If
fold$\le$max(table(classes)) then the
data will be split into fold disjoint sub
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.