glmSparseNet.cox: Call glmSparseNet model with Cox regression

In averissimo/glmSparseNetPaper:

Description

This is an auxiliary method for the analysis. It uses the run.cache method to cache the results and speed up analysis

Usage

glmSparseNet.cox(xdata, ydata, target.vars, alpha = 1,
  network = "correlation", xdata.digest = NULL,
  cache.prefix = "glmSparseNet.cache", force.recalc = FALSE,
  lambda.min.ratio = 0.001, ...)

Arguments

xdata	input matrix
ydata	Surival data.dataframe with time and status columns
target.vars	number of variables to target in calculations
network	network to use, could be a matrix, a degree vector or a string, see ?glmSparseNet
xdata.digest	sha256 checksum of xdata
cache.prefix	prefix for the cache files
force.recalc	force cache to be recalculated
...	additional parameters for glmnet

Value

a glmnet model

averissimo/glmSparseNetPaper documentation built on Jan. 25, 2021, 12:11 p.m.