lasso.tree.bic: Function to select the best beta according to BIC

In WangTJ/glidars: Cancer staging with meta-analysis

Description

For a vector of tuning parameters lambda, calculate all the beta and select the best one according to BIC criterion

Usage

lasso.tree.bic(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	feature one of the tumor
N	feature two of the tumor
y	time to failure
cen	censor indicator
lambda	tuning parameter of the fused group lasso penalty, a vector
inibeta	vector of initial values 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

a list including: the best solution of beta according to BIC, a vector of BIC values corresponding to the input lambda, and a "param" beta.seq including all the beta's

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