compare_by_calibrate: Compare high-dimensional Cox models by model calibration

In road2stat/hdnom: Benchmarking and Visualization Toolkit for Penalized Cox Models

Compare high-dimensional Cox models by model calibration

Description

Compare high-dimensional Cox models by model calibration

Usage

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)

road2stat/hdnom documentation built on March 14, 2024, 11:10 p.m.