xtune.control: Control function for xtune fitting

Description

Control function for xtune fitting.

Usage

xtune.control(
  alpha.est.init = NULL,
  max_s = 20,
  margin_s = 1e-05,
  maxstep = 100,
  margin = 0.001,
  maxstep_inner = 100,
  margin_inner = 0.001,
  compute.likelihood = FALSE,
  verbosity = FALSE,
  standardize = TRUE,
  intercept = TRUE
)

Value

A list of control objects after the checking.

Arguments

alpha.est.init: Initial values of alpha vector supplied to the algorithm. Alpha values are the hyper-parameters for the double exponential prior of regression coefficients, and it controls the prior variance of regression coefficients. Default is a vector of 0 with length p.
max_s: Maximum number of outer loop iterations for binary or multiclass outcomes. Default is 20.
margin_s: Convergence threshold of the outer loop for binary or multiclass outcomes. Default is 1e-5.
maxstep: Maximum number of iterations. Default is 100.
margin: Convergence threshold. Default is 1e-3.
maxstep_inner: Maximum number of iterations for the inner loop of the majorization-minimization algorithm. Default is 100.
margin_inner: Convergence threshold for the inner loop of the majorization-minimization algorithm. Default is 1e-3.
compute.likelihood: Should the function compute the marginal likelihood for hyper-parameters at each step of the update? Default is TRUE.
verbosity: Track algorithm update process? Default is FALSE.
standardize: Standardize X or not, same as the standardized option in glmnet.
intercept: Should intercept(s) be fitted (default=TRUE) or set to zero (FALSE), same as the intercept option in glmnet.