cleanUrl: "r-gelnet-usage"
description: "R의 generalized elasticnet (gelnet) 패키지 사용법에 대해 알아봅니다."

gelnet (Generalized ElasticNet) 사용법

Functions

gelnet

gelnet(X, y, l1, l2, nFeats=NULL, a=rep(1, n), d=rep(1, p),
	P=diag(p), m=rep(0, p), max.iter=100, eps=1e-05,
	w.init=rep(0, p), b.init=NULL, fix.bias=FALSE, silent=FALSE,
	balanced=FALSE, nonneg=FALSE
)

• X: n-by-p matrix of n samples in p dimensions
• y: n-by-1 vector of response values. Must be numeric vector for regression, factor with 2 levels for binary classification, or NULL for a one-class task.
• l1: coefficient for the L1-norm penalty
• l2: coefficient for the L2-norm penalty
• nFeats: alternative parameterization that returns the desired number of non-zero weights. Takes precedence over l1 if not NULL
• a: n-by-1 vector of sample weights (regression only)
• d: p-by-1 vector of feature weights
• P: p-by-p feature association penalty matrix
• m: p-by-1 vector of translation coefficients
• max.iter: maximum number of iterations
• eps: convergence precision
• w.init: initial parameter estimate for the weights
• b.init: initial parameter estimate for the bias term
• fix.bias: set to TRUE to prevent the bias term from being updated (Regression only) (default: FALSE)
• silent: set to TRUE to suppress run-time output to stdout (default: FALSE)