survFit | R Documentation |

Computes the predicted survivor function for a Cox proportional hazards model.

## S3 method for class 'mboost' survFit(object, newdata = NULL, ...) ## S3 method for class 'survFit' plot(x, xlab = "Time", ylab = "Probability", ...)

`object` |
an object of class |

`newdata` |
an optional data frame in which to look for variables with which to predict the survivor function. |

`x` |
an object of class |

`xlab` |
the label of the x axis. |

`ylab` |
the label of the y axis. |

`...` |
additional arguments passed to callies. |

If `newdata = NULL`

, the survivor function of the Cox proportional
hazards model is computed for the mean of the covariates used in the
`blackboost`

, `gamboost`

, or `glmboost`

call. The Breslow estimator is used for computing the baseline survivor
function. If `newdata`

is a data frame, the `predict`

method
of `object`

, along with the Breslow estimator, is used for computing the
predicted survivor function for each row in `newdata`

.

An object of class `survFit`

containing the following components:

`surv` |
the estimated survival probabilities at the time points
given in |

`time` |
the time points at which the survivor functions are evaluated. |

`n.event` |
the number of events observed at each time point given
in |

`gamboost`

, `glmboost`

and
`blackboost`

for model fitting.

library("survival") data("cancer", package = "survival") fm <- Surv(futime,fustat) ~ age + resid.ds + rx + ecog.ps fit <- glmboost(fm, data = ovarian, family = CoxPH(), control=boost_control(mstop = 500)) S1 <- survFit(fit) S1 newdata <- ovarian[c(1,3,12),] S2 <- survFit(fit, newdata = newdata) S2 plot(S1)

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