fit_lasso: Model selection for high-dimensional Cox models with lasso...

In hdnom: Benchmarking and Visualization Toolkit for Penalized Cox Models

Model selection for high-dimensional Cox models with lasso penalty

Description

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

Usage

fit_lasso(x, y, nfolds = 5L, rule = c("lambda.min", "lambda.1se"), seed = 1001)

Arguments

x	Data matrix.
y	Response matrix made by Surv.
nfolds	Fold numbers of cross-validation.
rule	Model selection criterion, "lambda.min" or "lambda.1se". See cv.glmnet for details.
seed	A random seed for cross-validation fold division.

Examples

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

fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11)

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.