coxgrad {glmnet} | R Documentation |

## 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)
```

### Arguments

`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 |

### 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.

### 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*version 4.1-8 Index]