R/1_1_model.R
fit_enet.Rd
Automatic model selection for highdimensional Cox models with elasticnet penalty, evaluated by penalized partiallikelihood.
fit_enet(x, y, nfolds = 5L, alphas = seq(0.05, 0.95, 0.05), rule = c("lambda.min", "lambda.1se"), seed = 1001, parallel = FALSE)
x  Data matrix. 

y  Response matrix made by 
nfolds  Fold numbers of crossvalidation. 
alphas  Alphas to tune in 
rule  Model selection criterion, 
seed  A random seed for crossvalidation fold division. 
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_enet(..., parallel = TRUE). fit < fit_enet( x, y, nfolds = 3, alphas = c(0.3, 0.7), rule = "lambda.1se", seed = 11 ) nom < as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2Year Overall Survival Probability" ) plot(nom)