coxgrad: Compute gradient for Cox model

In glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models

Compute gradient for Cox model

Description

Compute the gradient of the log partial likelihood at a particular fit for Cox model.

Usage

coxgrad(
  eta,
  y,
  w,
  std.weights = TRUE,
  diag.hessian = FALSE,
  cox.ties = c("breslow", "efron")
)

Arguments

eta	Fit vector (usually from glmnet at a particular lambda).
y	Survival response variable, must be a Surv or stratifySurv object.
w	Observation weights (default is all equal to 1).
std.weights	If TRUE (default), observation weights are standardized to sum to 1.
diag.hessian	If TRUE, compute the diagonal of the Hessian of the log partial likelihood as well. Default is FALSE.
cox.ties	Character; the method for handling ties. One of "breslow" (the current default) or "efron". The default will change to "efron" in glmnet 5.1 to match survival::coxph.

Details

Compute a gradient vector at the fitted vector for the log partial likelihood. This is like a residual vector, and useful for manual screening of predictors for glmnet in applications where p is very large (as in GWAS).

Uses the C++ coxdev library for computation, supporting both Breslow and Efron methods for ties, as well as stratified and (start, stop] data.

Value

A single gradient vector the same length as eta. If diag.hessian=TRUE, the diagonal of the Hessian is included as an attribute "diag_hessian".

See Also

coxnet.deviance

Examples

set.seed(1)
eta <- rnorm(10)
time <- runif(10, min = 1, max = 10)
d <- ifelse(rnorm(10) > 0, 1, 0)
y <- survival::Surv(time, d)
coxgrad(eta, y)

# return diagonal of Hessian as well
coxgrad(eta, y, diag.hessian = TRUE)

# example with (start, stop] data
y2 <- survival::Surv(time, time + runif(10), d)
coxgrad(eta, y2)

# example with strata
y2 <- stratifySurv(y, rep(1:2, length.out = 10))
coxgrad(eta, y2)

glmnet documentation built on May 4, 2026, 9:09 a.m.