coxgrad: 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")
)

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".

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.

Examples

Run this code

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)