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

fit_aenet(x, y, nfolds = 5L, alphas = seq(0.05, 0.95, 0.05),
  rule = c("lambda.min", "lambda.1se"), seed = c(1001, 1002),
  parallel = FALSE)

Arguments

x

Data matrix.

y

Response matrix made with Surv.

nfolds

Fold numbers of cross-validation.

alphas

Alphas to tune in cv.glmnet.

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.

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) # To enable parallel parameter tuning, first run: # library("doParallel") # registerDoParallel(detectCores()) # then set fit_aenet(..., parallel = TRUE). fit <- fit_aenet( x, y, nfolds = 3, alphas = c(0.3, 0.7), rule = "lambda.1se", seed = c(5, 7) ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)