`R/1_1_model.R`

`fit_alasso.Rd`

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

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

x | Data matrix. |
---|---|

y | Response matrix made by |

nfolds | Fold numbers of cross-validation. |

rule | Model selection criterion, |

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

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)