Description Usage Arguments Details Author(s) References Examples

Computes the conditional survival probability P(T > y|Z = z)

1 |

`time` |
The survival time of the process. |

`status` |
Censoring indicator of the total time of the process; 0 if the total time is censored and 1 otherwise. |

`covariate` |
Covariate values for obtaining estimates for the conditional probabilities. |

`delta` |
Censoring indicator of the covariate. |

`x` |
The first time (or covariate value) for obtaining estimates for the conditional probabilities. If missing, 0 will be used. |

`y` |
The total time for obtaining estimates for the conditional probabilities. |

`kernel` |
A character string specifying the desired kernel. See details below for possible options. Defaults to "gaussian" where the gaussian density kernel will be used. |

`bw` |
A single numeric value to compute a kernel density bandwidth. |

`lower.tail` |
logical; if FALSE (default), probabilities are P(T > y|Z = z) otherwise, P(T <= y|Z = z). |

Possible options for argument window are "gaussian", "epanechnikov", "tricube", "boxcar", "triangular", "quartic" or "cosine".

Luis Meira-Machado, Marta Sestelo and Gustavo Soutinho

R. Beran. Nonparametric regression with randomly censored survival data. Technical report, University of California, Berkeley, 1981.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
obj <- with(colonIDM, survIDM(time1, event1, Stime, event))
obj0 <- obj
#P(T>y|age=45)
library(KernSmooth)
h <- dpik(colonIDM$age)
Beran(time = obj0$Stime, status = obj0$event, covariate = colonIDM$age,
x = 45, y = 730, bw = h)
#P(T<=y|age=45)
Beran(time = obj0$Stime, status = obj0$event, covariate = colonIDM$age,
x = 45, y = 730, bw = h, lower.tail = TRUE)
``` |

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