lambda: Compute a lambda sequence for the regularization path
In lsgl: Linear Multiple Output Sparse Group Lasso

Description Usage Arguments Value Author(s)

Computes a decreasing lambda sequence of length d. The sequence ranges from a data determined maximal lambda λ_\textrm{max} to the user inputed lambda.min.

lambda(x, y, intercept = TRUE, weights = NULL, grouping = NULL,
  groupWeights = NULL, parameterWeights = NULL, alpha = 1, d = 100L,
  lambda.min, lambda.min.rel = FALSE,
  algorithm.config = lsgl.standard.config)

`x`	design matrix, matrix of size N \times p.
`y`	response matrix, matrix of size N \times K.
`intercept`	should the model include intercept parameters.
`weights`	sample weights, vector of size N \times K.
`grouping`	grouping of features, a factor or vector of length p. Each element of the factor/vector specifying the group of the feature.
`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.
`d`	the length of lambda sequence
`lambda.min`	the smallest lambda value in the computed sequence.
`lambda.min.rel`	is lambda.min relative to lambda.max ? (i.e. actual lambda min used is `lambda.min*lambda.max`, with `lambda.max` the computed maximal lambda value)
`algorithm.config`	the algorithm configuration to be used.