Compute gradient for Cox model
Compute the gradient of the log partial likelihood at a particular fit for Cox model.
coxgrad(eta, y, w, std.weights = TRUE, diag.hessian = FALSE)
eta |
Fit vector (usually from glmnet at a particular lambda). |
y |
Survival response variable, must be a |
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 |
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.
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".
coxnet.deviance
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)
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