# Fit a sparse group lasso regularization path.

### Description

Fit a sparse group lasso regularization path.

### Usage

 1 2 3 4  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

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