sgl_cv {sglOptim} | R Documentation |
Generic sparse group lasso cross validation using multiple possessors
sgl_cv(module_name, PACKAGE, data, parameterGrouping = NULL, groupWeights = NULL, parameterWeights = NULL, alpha, lambda, d = 100, compute_lambda = length(lambda) == 1, fold = 2, sampleGroups = NULL, cv.indices = list(), responses = NULL, max.threads = NULL, use_parallel = FALSE, algorithm.config = sgl.standard.config)
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 β. |
groupWeights |
the group weights, a vector of length |
parameterWeights |
a matrix of size q \times p. |
alpha |
the α value 0 for group lasso, 1 for lasso, between 0 and 1 gives a sparse group lasso penalty. |
lambda |
lambda.min relative to lambda.max (if |
d |
length of lambda sequence (ignored if |
compute_lambda |
should the lambda sequence be computed |
fold |
the fold of the cross validation, an integer larger than 1 and less than N+1.
Ignored if |
sampleGroups |
grouping of samples, the algorithm computing the cv.indices will try to equally divide the groups among the subsamples. |
cv.indices |
a list of indices of a cross validation splitting.
If |
responses |
a vector of responses to simplify and return (if NULL (deafult) no formating will be done) |
max.threads |
Deprecated (will be removed in 2018),
instead use |
use_parallel |
If |
algorithm.config |
the algorithm configuration to be used. |
Y.true |
the response, that is the |
responses |
content will depend on the C++ response class |
cv.indices |
the cross validation splitting used |
features |
number of features used in the models |
parameters |
number of parameters used in the models |
lambda |
the lambda sequence used. |
Martin Vincent