survfit.coxnet {glmnet} | R Documentation |

## Compute a survival curve from a coxnet object

### Description

Computes the predicted survivor function for a Cox proportional hazards model with elastic net penalty.

### Usage

```
## S3 method for class 'coxnet'
survfit(formula, s = NULL, ...)
```

### Arguments

`formula` |
A class |

`s` |
Value(s) of the penalty parameter lambda at which the survival
curve is required. Default is the entire sequence used to create the model.
However, it is recommended that |

`...` |
This is the mechanism for passing additional arguments like (i) x= and y= for the x and y used to fit the model, (ii) weights= and offset= when the model was fit with these options, (iii) arguments for new data (newx, newoffset, newstrata), and (iv) arguments to be passed to survfit.coxph(). |

### Details

To be consistent with other functions in `glmnet`

, if `s`

is not specified, survival curves are returned for the entire lambda
sequence. This is not recommended usage: it is best to call
`survfit.coxnet`

with a single value of the penalty parameter
for the `s`

option.

### Value

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.

### Examples

```
set.seed(2)
nobs <- 100; nvars <- 15
xvec <- rnorm(nobs * nvars)
xvec[sample.int(nobs * nvars, size = 0.4 * nobs * nvars)] <- 0
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)
fit1 <- glmnet(x, y, family = "cox")
# survfit object for Cox model where lambda = 0.1
sf1 <- survival::survfit(fit1, s = 0.1, x = x, y = y)
plot(sf1)
# example with new data
sf2 <- survival::survfit(fit1, s = 0.1, x = x, y = y, newx = x[1:3, ])
plot(sf2)
# example with strata
y2 <- stratifySurv(y, rep(1:2, length.out = nobs))
fit2 <- glmnet(x, y2, family = "cox")
sf3 <- survival::survfit(fit2, s = 0.1, x = x, y = y2)
sf4 <- survival::survfit(fit2, s = 0.1, x = x, y = y2,
newx = x[1:3, ], newstrata = c(1, 1, 1))
```

*glmnet*version 4.1-8 Index]