Automatic model selection for highdimensional Cox models with Mnet penalty, evaluated by penalized partiallikelihood.
fit_mnet(x, y, nfolds = 5L, gammas = c(1.01, 1.7, 3, 100), alphas = seq(0.05, 0.95, 0.05), eps = 1e04, max.iter = 10000L, seed = 1001, trace = FALSE, parallel = FALSE)
x  Data matrix. 

y  Response matrix made by 
nfolds  Fold numbers of crossvalidation. 
gammas  Gammas to tune in 
alphas  Alphas to tune in 
eps  Convergence threshhold. 
max.iter  Maximum number of iterations. 
seed  A random seed for crossvalidation fold division. 
trace  Output the crossvalidation parameter tuning
progress or not. Default is 
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)])[1:120, ] time < smart$TEVENT[1:120] event < smart$EVENT[1:120] y < survival::Surv(time, event) fit < fit_mnet( x, y, nfolds = 3, gammas = 3, alphas = c(0.3, 0.8), max.iter = 15000, seed = 1010 ) nom < as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2Year Overall Survival Probability" ) plot(nom)