`coxnet.deviance.Rd`

Compute the deviance (-2 log partial likelihood) for Cox model.

```
coxnet.deviance(
pred = NULL,
y,
x = NULL,
offset = NULL,
weights = NULL,
std.weights = TRUE,
beta = NULL
)
```

- pred
Fit vector or matrix (usually from glmnet at a particular lambda or a sequence of lambdas).

- y
Survival response variable, must be a

`Surv`

or`stratifySurv`

object.- x
Optional

`x`

matrix, to be supplied if`pred = NULL`

.- offset
Optional offset vector.

- weights
Observation weights (default is all equal to 1).

- std.weights
If TRUE (default), observation weights are standardized to sum to 1.

- beta
Optional coefficient vector/matrix, to be supplied if

`pred = NULL`

.

A vector of deviances, one for each column of predictions.

Computes the deviance for a single set of predictions, or for a matrix
of predictions. The user can either supply the predictions
directly through the `pred`

option, or by supplying the `x`

matrix
and `beta`

coefficients. Uses the Breslow approach to ties.

The function first checks if `pred`

is passed: if so, it is used as
the predictions. If `pred`

is not passed but `x`

and `beta`

are passed, then these values are used to compute the predictions. If
neither `x`

nor `beta`

are passed, then the predictions are all
taken to be 0.

`coxnet.deviance()`

is a wrapper: it calls the appropriate internal
routine based on whether the response is right-censored data or
(start, stop] survival data.

`coxgrad`

```
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)
coxnet.deviance(pred = eta, y = y)
#> [1] 23.53084
# if pred not provided, it is set to zero vector
coxnet.deviance(y = y)
#> [1] 24.66365
# example with x and beta
x <- matrix(rnorm(10 * 3), nrow = 10)
beta <- matrix(1:3, ncol = 1)
coxnet.deviance(y = y, x = x, beta = beta)
#> [1] 27.84795
# example with (start, stop] data
y2 <- survival::Surv(time, time + runif(10), d)
coxnet.deviance(pred = eta, y = y2)
#> [1] 5.859333
# example with strata
y2 <- stratifySurv(y, rep(1:2, length.out = 10))
coxnet.deviance(pred = eta, y = y2)
#> [1] 13.05457
```