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

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)