Coxnet-package: Regularized Cox Model
In Coxnet: Regularized Cox Model

Description Details Author(s) References Examples

This package fits a Cox model regularized with net (L1 and Laplacian), elastic-net (L1 and L2) or lasso (L1) penalty, and their adaptive forms, such as adaptive lasso and net adjusting for signs of linked coefficients. Moreover, it treats the number of non-zero coefficients as another tuning parameter and simultaneously selects with the regularization parameter lambda.

In addition, it fits a varying coefficient Cox model by kernel smoothing, incorporated with the aforementioned penalties.

The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients.

Package:	Coxnet
Type:	Package
Version:	0.2
Date:	2015-12-09
License:	GPL (>= 2)

Functions: Coxnet, loCoxnet, print.Coxnet, coxsplit

Xiang Li, Donglin Zeng and Yuanjia Wang
Maintainer: Xiang Li <xl2473@columbia.edu>

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010
http://www.jstatsoft.org/v33/i01/
Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5) 1-13
http://www.jstatsoft.org/v39/i05/
Sun, H., Lin, W., Feng, R., and Li, H. (2014) Network-regularized high-dimensional cox regression for analysis of genomic data, Statistica Sinica.
http://www3.stat.sinica.edu.tw/statistica/j24n3/j24n319/j24n319.html
van Houwelingen, H. C., Bruinsma, T., Hart, A. A., van't Veer, L. J., & Wessels, L. F. (2006) Cross-validated Cox regression on microarray gene expression data. Statistics in medicine, 25(18), 3201-3216.
http://onlinelibrary.wiley.com/doi/10.1002/sim.2353/full

set.seed(1213)
N=100;p=30;p1=5
x=matrix(rnorm(N*p),N,p)
beta=rnorm(p1)
xb=x[,1:p1]
ty=rexp(N,exp(xb))
tcens=rbinom(n=N,prob=.3,size=1)  # censoring indicator
y=cbind(time=ty,status=1-tcens)
fiti=Coxnet(x,y,penalty="Lasso")  # Lasso