covCdaC2 {iilasso} | R Documentation |
(Experimental) Optimize an ULasso linear regression problem by coordinate descent algorithm using a covariance matrix
covCdaC2(Gamma, gamma, lambda, R, init_beta, delta = 0, maxit = 10000, eps = 1e-04, warm = "lambda", strong = TRUE)
Gamma |
covariance matrix of explanatory variables |
gamma |
covariance vector of explanatory and objective variables |
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