Description Usage Arguments Value Examples
Fit a logistic regression model using a design matrix
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X_tilde |
standardized matrix of explanatory variables |
y |
vector of objective variable |
lambda.min.ratio |
ratio of max lambda and min lambda |
nlambda |
the number of lambda (ignored if lambda is specified) |
lambda |
lambda sequence |
delta |
ratio of regularization (exclusive penalty / l1 penalty) (default: 0) |
alpha |
mixing parameter of regularization of l1 and exclusive penalty terms (delta = (1 - alpha) / alpha) |
R |
matrix using exclusive penalty term |
funcR |
function of R (input: X, output: R) |
maxit |
max iteration (default: 1e+4) |
eps |
convergence threshold for optimization (default: 1e-4) |
warm |
warm start direction: "lambda" (default) or "delta" |
init.beta |
initial values of beta |
strong |
whether use strong screening (default) or not |
sparse |
whether use sparse matrix or not (default) |
impl |
implementation language of optimization: "cpp" (default) or "r" |
abs |
(experimental) whether use absolute value of beta (default) or not |
lasso model
beta_standard |
standardized coefficients |
lambda |
regularization parameters |
alpha |
alpha defined above |
delta |
delta defined above |
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