Description Usage Arguments Value Author(s)
Fit a sparse group lasso regularization path.
1 2 3 4 | sgl_fit(module_name, PACKAGE, data, covariateGrouping,
groupWeights, parameterWeights, alpha, lambda,
return = 1:length(lambda),
algorithm.config = sgl.standard.config)
|
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. |
beta |
the fitted parameters – a list of length
|
loss |
the values of the loss function |
objective |
the values of the objective function (i.e. loss + penalty) |
lambda |
the lambda values used |
Martin Vincent
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