fit_snet: Model selection for high-dimensional Cox models with Snet...

In hdnom: Benchmarking and Visualization Toolkit for Penalized Cox Models

Model selection for high-dimensional Cox models with Snet penalty

Description

Automatic model selection for high-dimensional Cox models with Snet penalty, evaluated by penalized partial-likelihood.

Usage

fit_snet(
  x,
  y,
  nfolds = 5L,
  gammas = c(2.01, 2.3, 3.7, 200),
  alphas = seq(0.05, 0.95, 0.05),
  eps = 1e-04,
  max.iter = 10000L,
  seed = 1001,
  trace = FALSE,
  parallel = FALSE
)

Arguments

x	Data matrix.
y	Response matrix made by Surv.
nfolds	Fold numbers of cross-validation.
gammas	Gammas to tune in cv.ncvsurv.
alphas	Alphas to tune in cv.ncvsurv.
eps	Convergence threshhold.
max.iter	Maximum number of iterations.
seed	A random seed for cross-validation fold division.
trace	Output the cross-validation parameter tuning progress or not. Default is FALSE.
parallel	Logical. Enable parallel parameter tuning or not, default is FALSE. To enable parallel tuning, load the doParallel package and run registerDoParallel() with the number of CPU cores before calling this function.

Examples

data("smart")
x <- as.matrix(smart[, -c(1, 2)])
time <- smart$TEVENT
event <- smart$EVENT
y <- survival::Surv(time, event)

fit <- fit_snet(
  x, y,
  nfolds = 3,
  gammas = 3.7, alphas = c(0.3, 0.8),
  max.iter = 15000, seed = 1010
)

nom <- as_nomogram(
  fit, x, time, event,
  pred.at = 365 * 2,
  funlabel = "2-Year Overall Survival Probability"
)

plot(nom)

hdnom documentation built on April 24, 2023, 9:09 a.m.