Fit a sparse group lasso regularization path.

Description

Fit a sparse group lasso regularization path.

Usage

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  sgl_fit(module_name, PACKAGE, data, covariateGrouping,
    groupWeights, parameterWeights, alpha, lambda,
    return = 1:length(lambda),
    algorithm.config = sgl.standard.config)

Arguments

call_sym

reference to objective specific C++ routines

data
covariateGrouping

grouping of covariates, a vector of length p. Each element of the vector specifying the group of the covariate.

groupWeights

the group weights, a vector of length m (the number of groups).

parameterWeights

a matrix of size K \times (p).

alpha

the α 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.

return

the indices of lambda values for which to return a the fitted parameters.

algorithm.config

the algorithm configuration to be used.

Value

beta

the fitted parameters – a list of length length(lambda) with each entry a matrix of size K\times (p+1) holding the fitted parameters

loss

the values of the loss function

objective

the values of the objective function (i.e. loss + penalty)

lambda

the lambda values used

Author(s)

Martin Vincent