Auxiliary function for controlling the islasso model fitting.
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)optional. The fixed value of dispersion parameter. If -1 (default) it is estimated from the data
tollerance value to declare convergence, dafault to 1e-5
maximum number of iterations, default to 1000
if TRUE (default), the covariates are standardized prior to fitting the model. However the coefficients are always returned on the original scale.
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).
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.
optional, the seed to be used to split the dataframe and to perform cross validation. Useful to make reproducible the results.
experimental, if TRUE the adaptive LASSO is implemented.
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.
optional, starting values for the regression coefficients. If NULL, the point estimates from glmnet are used.
optional, starting value for the estimates covariance matrix, If NULL, the identity matrix is used.
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.
Maintainer: Gianluca Sottile <gianluca.sottile@unipa.it>