Description Usage Arguments Value
(Experimental) Optimize an ULasso logistic regression problem by coordinate descent algorithm using a design matrix
1 2 | logitCdaC2(X_tilde, y, lambda, R, init_beta, delta = 0, maxit = 10000,
eps = 1e-04, warm = "lambda", strong = TRUE)
|
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 |
standardized beta
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