xtune.control: Control function for xtune fitting

In ChubingZeng/classo: Regularized Regression with Differential Penalties Integrating External Information

Description

Control function for xtune fitting.

Usage

xtune.control(alpha.init = NULL, maxstep = 100, tolerance = 0.001,
  maxstep_inner = 50, tolerance_inner = 0.1, compute.likelihood = FALSE,
  verbosity = FALSE, standardize = TRUE, intercept = TRUE)

Arguments

alpha.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.
maxstep	Maximum number of iterations. Default is 100.
tolerance	Convergence threshold. Default is 1e-4.
maxstep_inner	Maximum number of iterations for the inner loop of the majorization-minimization algorithm.
tolerance_inner	Convergence threshold for the inner loop of the majorization-minimization algorithm.
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

ChubingZeng/classo documentation built on June 4, 2019, 12:37 p.m.