calibrate: Calibrate high-dimensional Cox models

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

Calibrate high-dimensional Cox models

Description

Calibrate high-dimensional Cox models

Usage

calibrate(
  x,
  time,
  event,
  model.type = c("lasso", "alasso", "flasso", "enet", "aenet", "mcp", "mnet", "scad",
    "snet"),
  alpha,
  lambda,
  pen.factor = NULL,
  gamma,
  lambda1,
  lambda2,
  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 type to calibrate. Could be one of "lasso", "alasso", "flasso", "enet", "aenet", "mcp", "mnet", "scad", or "snet".
alpha	Value of the elastic-net mixing parameter alpha for enet, aenet, mnet, and snet models. For lasso, alasso, mcp, and scad models, please set alpha = 1. alpha=1: lasso (l1) penalty; alpha=0: ridge (l2) penalty. Note that for mnet and snet models, alpha can be set to very close to 0 but not 0 exactly.
lambda	Value of the penalty parameter lambda to use in the model fits on the resampled data. From the Cox model you have built.
pen.factor	Penalty factors to apply to each coefficient. From the built adaptive lasso or adaptive elastic-net model.
gamma	Value of the model parameter gamma for MCP/SCAD/Mnet/Snet models.
lambda1	Value of the penalty parameter lambda1 for fused lasso model.
lambda2	Value of the penalty parameter lambda2 for fused lasso model.
method	Calibration method. Options including "fitting", "bootstrap", "cv", and "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 resampling.
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
y <- survival::Surv(time, event)

# Fit Cox model with lasso penalty
fit <- fit_lasso(x, y, nfolds = 5, rule = "lambda.1se", seed = 11)

# Model calibration by fitting the original data directly
cal.fitting <- calibrate(
  x, time, event,
  model.type = "lasso",
  alpha = 1, lambda = fit$lambda,
  method = "fitting",
  pred.at = 365 * 9, ngroup = 5,
  seed = 1010
)

# Model calibration by 5-fold cross-validation
cal.cv <- calibrate(
  x, time, event,
  model.type = "lasso",
  alpha = 1, lambda = fit$lambda,
  method = "cv", nfolds = 5,
  pred.at = 365 * 9, ngroup = 5,
  seed = 1010
)

print(cal.fitting)
summary(cal.fitting)
plot(cal.fitting)

print(cal.cv)
summary(cal.cv)
plot(cal.cv)

# # Test fused lasso, SCAD, and Mnet models
# data(smart)
# x = as.matrix(smart[, -c(1, 2)])[1:500, ]
# time = smart$TEVENT[1:500]
# event = smart$EVENT[1:500]
# y = survival::Surv(time, event)
#
# set.seed(1010)
# cal.fitting = calibrate(
#   x, time, event, model.type = "flasso",
#   lambda1 = 5, lambda2 = 2,
#   method = "fitting",
#   pred.at = 365 * 9, ngroup = 5,
#   seed = 1010)
#
# cal.boot = calibrate(
#   x, time, event, model.type = "scad",
#   gamma = 3.7, alpha = 1, lambda = 0.03,
#   method = "bootstrap", boot.times = 10,
#   pred.at = 365 * 9, ngroup = 5,
#   seed = 1010)
#
# cal.cv = calibrate(
#   x, time, event, model.type = "mnet",
#   gamma = 3, alpha = 0.3, lambda = 0.03,
#   method = "cv", nfolds = 5,
#   pred.at = 365 * 9, ngroup = 5,
#   seed = 1010)
#
# cal.repcv = calibrate(
#   x, time, event, model.type = "flasso",
#   lambda1 = 5, lambda2 = 2,
#   method = "repeated.cv", nfolds = 5, rep.times = 3,
#   pred.at = 365 * 9, ngroup = 5,
#   seed = 1010)
#
# print(cal.fitting)
# summary(cal.fitting)
# plot(cal.fitting)
#
# print(cal.boot)
# summary(cal.boot)
# plot(cal.boot)
#
# print(cal.cv)
# summary(cal.cv)
# plot(cal.cv)
#
# print(cal.repcv)
# summary(cal.repcv)
# plot(cal.repcv)

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