fit_snet {hdnom} | R Documentation |
Automatic model selection for high-dimensional Cox models with Snet penalty, evaluated by penalized partial-likelihood.
fit_snet(x, y, nfolds = 5L, gammas = c(2.01, 2.3, 3.7, 200), alphas = seq(0.05, 0.95, 0.05), eps = 1e-04, max.iter = 10000L, seed = 1001, trace = FALSE, parallel = FALSE)
x |
Data matrix. |
y |
Response matrix made by |
nfolds |
Fold numbers of cross-validation. |
gammas |
Gammas to tune in |
alphas |
Alphas to tune in |
eps |
Convergence threshhold. |
max.iter |
Maximum number of iterations. |
seed |
A random seed for cross-validation fold division. |
trace |
Output the cross-validation parameter tuning
progress or not. Default is |
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)])[1:120, ] time <- smart$TEVENT[1:120] event <- smart$EVENT[1:120] y <- survival::Surv(time, event) fit <- fit_snet( x, y, nfolds = 3, gammas = 3.7, alphas = c(0.3, 0.8), max.iter = 15000, seed = 1010 ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)