`survfit.cv.glmnet.Rd`

Computes the predicted survivor function for a Cox proportional hazards model with elastic net penalty from a cross-validated glmnet model.

```
# S3 method for cv.glmnet
survfit(formula, s = c("lambda.1se", "lambda.min"), ...)
```

- formula
A class

`cv.glmnet`

object. The object should have been fit with`family = "cox"`

.- s
Value(s) of the penalty parameter lambda at which predictions are required. Default is the value s="lambda.1se" stored on the CV object. Alternatively s="lambda.min" can be used. If s is numeric, it is taken as the value(s) of lambda to be used.

- ...
Other arguments to be passed to

`survfit.coxnet`

.

If `s`

is a single value, an object of class "survfitcox"
and "survfit" containing one or more survival curves. Otherwise, a list
of such objects, one element for each value in `s`

.
Methods defined for survfit objects are print, summary and plot.

This function makes it easier to use the results of cross-validation to compute a survival curve.

```
set.seed(2)
nobs <- 100; nvars <- 15
xvec <- rnorm(nobs * nvars)
x <- matrix(xvec, nrow = nobs)
beta <- rnorm(nvars / 3)
fx <- x[, seq(nvars / 3)] %*% beta / 3
ty <- rexp(nobs, exp(fx))
tcens <- rbinom(n = nobs, prob = 0.3, size = 1)
y <- survival::Surv(ty, tcens)
cvfit <- cv.glmnet(x, y, family = "cox")
# default: s = "lambda.1se"
survival::survfit(cvfit, x = x, y = y)
#> Call: survfit.cv.glmnet(formula = cvfit, x = x, y = y)
#>
#> n events median
#> [1,] 100 25 3.53
# s = "lambda.min"
survival::survfit(cvfit, s = "lambda.min", x = x, y = y)
#> Call: survfit.cv.glmnet(formula = cvfit, s = "lambda.min", x = x, y = y)
#>
#> n events median
#> [1,] 100 25 3.53
```