is.control: Auxiliary for controlling islasso model fitting
In islasso: The Induced Smoothed Lasso

is.control

R Documentation

Auxiliary for controlling islasso model fitting

Description

Auxiliary function for controlling the islasso model fitting.

Usage

is.control(sigma2 = -1, tol = 1E-05, itmax = 1E+3, stand = TRUE,
  trace = 0, nfolds = 5, seed = NULL, adaptive = FALSE, g = .5,
  b0 = NULL, V0 = NULL, c = .5)

Arguments

`sigma2`	optional. The fixed value of dispersion parameter. If -1 (default) it is estimated from the data
`tol`	tollerance value to declare convergence, dafault to 1e-5
`itmax`	maximum number of iterations, default to 1000
`stand`	if TRUE (default), the covariates are standardized prior to fitting the model. However the coefficients are always returned on the original scale.
`trace`	Should the iterative procedure be printed? 0: no printing, 1 = compact printing, 2 = enlarged printing, 3 = compact printing including Fisher scoring information (only used in glm family).
`nfolds`	if `lambda` is unspecified in `islasso`, the number of folds to be used to perform cross valdation. Default to 5, and `nfolds>2` is allowed. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. `nfolds` is ignored if `lambda` is supplied.
`seed`	optional, the seed to be used to split the dataframe and to perform cross validation. Useful to make reproducible the results.
`adaptive`	experimental, if TRUE the adaptive LASSO is implemented.
`g`	a value belonging to the interval [0, 1]. Classical BIC is returned by letting g = 0 (default value), whereas extended BIC corresponds to the case g = 0.5.
`b0`	optional, starting values for the regression coefficients. If NULL, the point estimates from `glmnet` are used.
`V0`	optional, starting value for the estimates covariance matrix, If NULL, the identity matrix is used.
`c`	the weight of the mixture in the induced smoothed lasso, the default is `c = .5`. `c = -1` means to compute it at each step of the iterative algorithm.