sgl_predict: Predict

In sglOptim: Generic Sparse Group Lasso Solver

Description

Predict and return responses as defined in the module.

Usage

sgl_predict(module_name, PACKAGE, object, data, responses = NULL,
  auto_response_names = TRUE, ...)

Arguments

module_name	reference to objective specific C++ routines.
PACKAGE	name of the calling package.
object	a sgl object containing a list of estimated models.
data	a list of data objects – will be parsed to the specified module.
responses	a vector of responses to simplify and return (if NULL (deafult) no formating will be done)
auto_response_names	set response names
...	not used.

Details

If no formating is done (i.e. if responses = NULL) then the responses field contains a list of lists structured in the following way:

• sample 1

  • model (lambda) index 1

    • response elements

  • model (lambda) index 2

    • response elements

  • ...

• sample 2

  • model (lambda) index 1

    • response elements

  • model (lambda) index 2

    • response elements

  • ...

• ...

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.

• scalar: a matrix of size n \times d with the responses (where n is the number of samples and d the length of the lambda sequence).

• vector: a list of length d with each element a matrix of dimension n \times q containing the responses for the corresponding model (where q is the dimension of the response).

• matrix: a list with format samples -> models - > the response matrix.

Value

responses	list of lists structured as described in details. Content of the response elements will depend on the C++ response class
lambda	the lambda sequence used.

Author(s)

Martin Vincent

sglOptim documentation built on May 8, 2019, 1:02 a.m.