calibrate {hdnom} | R Documentation |
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 |
event |
Status indicator, normally 0 = alive, 1 = dead.
Must be of the same length with the number of rows as |
model.type |
Model type to calibrate. Could be one of |
alpha |
Value of the elastic-net mixing parameter alpha for
|
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 |
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 |
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)