R: Compute a survival curve from a cv.glmnet object

survfit.cv.glmnet {glmnet} | R Documentation

## Compute a survival curve from a cv.glmnet object

### Description

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

### Usage

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

### Arguments

`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`.

### Details

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

### 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)
    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)

    # s = "lambda.min"
    survival::survfit(cvfit, s = "lambda.min", x = x, y = y)

[Package glmnet version 4.1-2 Index]