lasso.tree: The core function for lasso-tree with meta-analysis

In WangTJ/glidars: Cancer staging with meta-analysis

Description

Find the best estimate of parameter beta's, for tuning parameters lambda

Usage

lasso.tree(G, T, N, y, cen, lambda, inibeta = NULL, trace = TRUE,
  maxiter = 200, eps = 1e-04, w_select = "plainCox", w = NULL)

Arguments

G	label of dataset
T, N	features one of the tumor
y	time to failure
cen	censor indicator
lambda	tuning parameter of the fused group lasso penalty, could be a single value or a vector
inibeta	vector of initial guess of beta's
trace	boolen varable whether to show the process of calculation
maxiter	max number of iteration
eps	tolerance
w_select	the method to select the adaptive weight w, "plainCox" or "preDefine"
w	adaptive weight w

Value

the estimate of lambda in "param" class (see as.param for details)

WangTJ/glidars documentation built on Jan. 20, 2021, 6:32 p.m.