logitCdaC2: (Experimental) Optimize an ULasso logistic regression problem...

Description Usage Arguments Value

Description

(Experimental) Optimize an ULasso logistic regression problem by coordinate descent algorithm using a design matrix

Usage

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logitCdaC2(X_tilde, y, lambda, R, init_beta, delta = 0, maxit = 10000,
  eps = 1e-04, warm = "lambda", strong = TRUE)

Arguments

X_tilde

standardized matrix of explanatory variables

y

vector of objective variable

lambda

lambda sequence

R

matrix using exclusive penalty term

init_beta

initial values of beta

delta

ratio of regularization between l1 and exclusive penalty terms

maxit

max iteration

eps

convergence threshold for optimization

warm

warm start direction: "lambda" (default) or "delta"

strong

whether use strong screening or not

Value

standardized beta


tkdmah/iilasso documentation built on May 17, 2019, 6:38 a.m.