Description Usage Arguments Details Value Author(s)
Subsampling procedure with support parallel computations.
1 2 3 4 5 6 | sgl_subsampling(module_name, PACKAGE, data, parameterGrouping = NULL,
groupWeights = NULL, parameterWeights = NULL, alpha, lambda,
d = 100, compute_lambda = length(lambda) == 1, training = NULL,
test = NULL, responses = NULL, auto_response_names = TRUE,
collapse = FALSE, 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 |
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 |
responses |
a vector of responses to simplify and return (if NULL (deafult) no formating will be done) |
auto_response_names |
set response names |
collapse |
if |
max.threads |
Deprecated (will be removed in 2018),
instead use |
use_parallel |
If |
algorithm.config |
the algorithm configuration to be used. |
If no formating is done (i.e. if responses = NULL
)
then the responses
field contains a list of lists structured in the following way:
subsamples 1:
sample test[[1]][1]
model (lambda) index 1
response elements
model (lambda) index 2
response elements
...
sample test[[1]][2]
model (lambda) index 1
response elements
model (lambda) index 2
response elements
...
...
subsamples 2: ...
If responses = "rname"
with rname
the name of the response then a list at responses$rname
will be returned.
The content of the list will depend on the type of the response.
vector A list with format subsamples -> models -> matrix of dimension n_i \times q containing the responses for the corresponding model and subsample (where q is the dimension of the response).
matrix A list with format subsamples -> samples -> models - > the response matrix.
Y.true |
the response, that is the |
responses |
content will depend on the C++ response class |
features |
number of features used in the models |
parameters |
number of parameters used in the models |
lambda |
the lambda sequences used (a vector or list of length |
Martin Vincent
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