fit_flasso {hdnom} | R Documentation |
Automatic model selection for high-dimensional Cox models with fused lasso penalty, evaluated by cross-validated likelihood.
fit_flasso(x, y, nfolds = 5L, lambda1 = c(0.001, 0.05, 0.5, 1, 5), lambda2 = c(0.001, 0.01, 0.5), maxiter = 25, epsilon = 0.001, seed = 1001, trace = FALSE, parallel = FALSE, ...)
x |
Data matrix. |
y |
Response matrix made by |
nfolds |
Fold numbers of cross-validation. |
lambda1 |
Vector of lambda1 candidates.
Default is |
lambda2 |
Vector of lambda2 candidates.
Default is |
maxiter |
The maximum number of iterations allowed.
Default is |
epsilon |
The convergence criterion.
Default is |
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
|
... |
The cross-validation procedure used in this function is the
approximated cross-validation provided by the penalized
package. Be careful dealing with the results since they might be more
optimistic than a traditional CV procedure. This cross-validation
method is more suitable for datasets with larger number of observations,
and a higher number of cross-validation folds.
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_flasso( x, y, lambda1 = c(1, 10), lambda2 = c(0.01), nfolds = 3, seed = 11 ) nom <- as_nomogram( fit, x, time, event, pred.at = 365 * 2, funlabel = "2-Year Overall Survival Probability" ) plot(nom)