glmnet (version 4.1-8)

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)

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.

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 Breslow approach to ties.

This function is essentially a wrapper: it checks whether the response provided is right-censored or (start, stop] survival data, and calls the appropriate internal routine.

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)