rsurv {ipred} | R Documentation |

## Simulate Survival Data

### Description

Simulation Setup for Survival Data.

### Usage

rsurv(N, model=c("A", "B", "C", "D", "tree"), gamma=NULL, fact=1, pnon=10,
gethaz=FALSE)

### Arguments

`N` |
number of observations. |

`model` |
type of model. |

`gamma` |
simulate censoring time as runif(N, 0, gamma). Defaults to
`NULL` (no censoring). |

`fact` |
scale parameter for `model=tree` . |

`pnon` |
number of additional non-informative variables for the tree
model. |

`gethaz` |
logical, indicating wheather the hazard rate for each
observation should be returned. |

### Details

Simulation setup similar to configurations used in LeBlanc and Crowley
(1992) or Keles and Segal (2002) as well as a tree model used in Hothorn et
al. (2002). See Hothorn et al. (2003) for the details.

### Value

A data frame with elements `time`

, `cens`

, `X1`

...
`X5`

. If `pnon`

> 0, additional noninformative covariables are
added. If `gethaz=TRUE`

, the `hazard`

attribute returns the hazard
rates.

### References

M. LeBlanc and J. Crowley (1992), Relative Risk Trees for
Censored Survival Data. *Biometrics* **48**, 411–425.

S. Keles and M. R. Segal (2002), Residual-based tree-structured
survival analysis. *Statistics in Medicine*, **21**, 313–326.

Torsten Hothorn, Berthold Lausen, Axel Benner and Martin
Radespiel-Troeger (2004), Bagging Survival Trees.
*Statistics in Medicine*, **23**(1), 77–91.

### Examples

# 3*X1 + X2
simdat <- rsurv(500, model="C")
coxph(Surv(time, cens) ~ ., data=simdat)

[Package

*ipred* version 0.8-1

Index]