Est.ALASSO.GLMNET: A wrapper for adaptive lasso

In michaelyanwang/divideconquer: Divide-and-conquer adaptive lasso using least square approximation

Description

Est.ALASSO.GLMNET fits adaptive lasso based on glmnet. The best lambda (penalizing factor) is chosen by BIC or modified BIC.

Usage

Est.ALASSO.GLMNET(data, BIC.factor = 0.1, fam0 = "binomial", w.b = NULL,
  lambda.grid, modBIC = T)

Arguments

data	matrix or data frame. For non-survival outcome, first column = response, rest = design matrix. For continuous-time survival outcome, first column = time, second column = delta (0/1), rest = design matrix.
BIC.factor	factor in modified BIC, BIC = -2 loglikelihood + df * N^BIC.factor
fam0	family of the response, taking "binomial", "Poisson", "Cox"
w.b	w.b used to penalize adaptive lasso. If null, a glm/Cox model will be fitted and 1/abs(coefficients) will be used as w.b
lambda.grid	the grid of lambda to put into glmnet
modBIC	if the program also finds the lamda that minimizes the modified BIC. The default is TRUE.

Value

bhat.BIC adaptive lasso estimator using BIC and bhat.modBIC adaptive lasso estimator using modified BIC. lambda.BIC and lambda.modBIC return the optimal lambda chosen by BIC and modified BIC.

Author(s)

Yan Wang, Tianxi Cai

Examples

Est.ALASSO.Approx.GLMNET(ynew,xnew,bini,N.adj)

michaelyanwang/divideconquer documentation built on Aug. 16, 2019, 10:11 a.m.