Model selection for high-dimensional Cox models with adaptive lasso penalty

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

Usage

fit_alasso(
  x,
  y,
  nfolds = 5L,
  rule = c("lambda.min", "lambda.1se"),
  seed = c(1001, 1002)
)

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

Two random seeds for cross-validation fold division in two estimation steps.

Examples

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

fit <- fit_alasso(x, y, nfolds = 3, rule = "lambda.1se", seed = c(7, 11))

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

plot(nom)