compare_by_calibrate {hdnom} | R Documentation |
Compare high-dimensional Cox models by model calibration
compare_by_calibrate(x, time, event, model.type = c("lasso", "alasso", "flasso", "enet", "aenet", "mcp", "mnet", "scad", "snet"), method = c("fitting", "bootstrap", "cv", "repeated.cv"), boot.times = NULL, nfolds = NULL, rep.times = NULL, pred.at, ngroup = 5, seed = 1001, trace = TRUE)
x |
Matrix of training data used for fitting the model; on which to run the calibration. |
time |
Survival time.
Must be of the same length with the number of rows as |
event |
Status indicator, normally 0 = alive, 1 = dead.
Must be of the same length with the number of rows as |
model.type |
Model types to compare. Could be at least two of
|
method |
Calibration method.
Could be |
boot.times |
Number of repetitions for bootstrap. |
nfolds |
Number of folds for cross-validation and repeated cross-validation. |
rep.times |
Number of repeated times for repeated cross-validation. |
pred.at |
Time point at which calibration should take place. |
ngroup |
Number of groups to be formed for calibration. |
seed |
A random seed for cross-validation fold division. |
trace |
Logical. Output the calibration progress or not.
Default is |
data(smart) x <- as.matrix(smart[, -c(1, 2)]) time <- smart$TEVENT event <- smart$EVENT # Compare lasso and adaptive lasso by 5-fold cross-validation cmp.cal.cv <- compare_by_calibrate( x, time, event, model.type = c("lasso", "alasso"), method = "fitting", pred.at = 365 * 9, ngroup = 5, seed = 1001 ) print(cmp.cal.cv) summary(cmp.cal.cv) plot(cmp.cal.cv)