fit_aenet {hdnom} | R Documentation |
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)
x |
Data matrix. |
y |
Response matrix made with |
nfolds |
Fold numbers of cross-validation. |
alphas |
Alphas to tune in |
rule |
Model selection criterion, |
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
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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)