cov_cda_r {iilasso} | R Documentation |
Optimize a linear regression model by coordinate descent algorithm using a covariance matrix with R
cov_cda_r(Gamma, gamma, lambda, R, init.beta, delta, maxit, eps, warm, strong, sparse)
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 |
sparse |
whether use sparse matrix or not |
standardized beta