R/1_1_model.R
fit_aenet.Rd
Automatic model selection for highdimensional Cox models with adaptive elasticnet penalty, evaluated by penalized partiallikelihood.
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 crossvalidation. 
alphas  Alphas to tune in 
rule  Model selection criterion, 
seed  Two random seeds for crossvalidation fold division in two estimation steps. 
parallel  Logical. Enable parallel parameter tuning or not,
default is FALSE. To enable parallel tuning, load the

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 = "2Year Overall Survival Probability" ) plot(nom)