compare_by_calibrate function

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
)

Arguments

• 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 x.
• event: Status indicator, normally 0 = alive, 1 = dead. Must be of the same length with the number of rows as x.
• model.type: Model types to compare. Could be at least two of "lasso", "alasso", "flasso", "enet", "aenet", "mcp", "mnet", "scad", or "snet".
• method: Calibration method. Could be "bootstrap", "cv", or "repeated.cv".
• 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 TRUE.

Examples

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)