Objects of class "lassoControl" define options such as bounds on the Hessian,
convergence criteria and output management for the Group Lasso
algorithm, `lasso()`

.

For the convergence criteria see chapter 8.2.3.2 of Gill et al. (1981).

Objects can be created by calls of the form `lassoControl(...)`

`save.x`

a logical indicating whether the design matrix should be saved.

`update.hess`

should the hessian be updated in each iteration ("always")? update.hess = "lambda" will update the Hessian once for each component of the penalty parameter "lambda" based on the parameter estimates corresponding to the previous value of the penalty parameter.

`update.every`

Only used if update.hess = "lambda". E.g. set to 3 if you want to update the Hessian only every third grid point.

`inner.loops`

How many loops should be done (at maximum) when solving only the active set (without considering the remaining predictors). Useful if the number of predictors is large. Set to 0 if no inner loops should be performed.

`line.search`

Should line searches be performed?

`max.iter`

Maximal number of loops through all groups

`tol`

convergence tolerance; the smaller the more precise.

`lower`

lower bound for the diagonal approximation of the corresponding block submatrix of the Hessian of the negative log-likelihood function.

`upper`

upper bound for the diagonal approximation of the corresponding block submatrix of the Hessian of the negative log-likelihood function.

`beta`

scaling factor

*β < 1*of the Armijo line search.`sigma`

*0 < σ < 1*used in the Armijo line search.`trace`

integer.

`1`

prints the current lambda value,`2`

prints the improvement in the objective function after each sweep through all the parameter groups and additional information.

Philip E. Gill, Walter Murray and Margaret H. Wright (1981)
*Practical Optimization*, Academic Press.

Dimitri P. Bertsekas (2003)
*Nonlinear Programming*, Athena Scientific.

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