fit_snet {hdnom}

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)