cox.adapt {extremefit} | R Documentation |

Compute the extreme quantile procedure for Cox model

cox.adapt(X, cph, cens = rep(1, length(X)), data = rep(0, length(X)), initprop = 1/10, gridlen = 100, r1 = 1/4, r2 = 1/20, CritVal = 10)

`X` |
a numeric vector of data values. |

`cph` |
an output object of the function coxph from the package survival. |

`cens` |
a binary vector corresponding to the censored values. |

`data` |
a data frame containing the covariates values. |

`initprop` |
the initial proportion at which we begin to test the model. |

`gridlen` |
the length of the grid for which the test is done. |

`r1` |
a proportion value of the data from the right that we skip in the test statistic. |

`r2` |
a proportion value of the data from the left that we skip in the test statistic. |

`CritVal` |
the critical value assiociated to procedure. |

Given a vector of data, a vector of censorship and a data frame of covariates, this function compute the adaptive procedure described in Grama and Jaunatre (2018).

We suppose that the data are in the domain of attraction of the Frechet-Pareto type and that the hazard are somewhat proportionals. Otherwise, the procedure will not work.

`coefficients` |
the coefficients of the coxph procedure. |

`Xsort` |
the sorted vector of the data. |

`sortcens` |
the sorted vector of the censorship. |

`sortebz` |
the sorted matrix of the covariates. |

`ch` |
the Hill estimator associated to the baseline function. |

`TestingGrid` |
the grid used for the statistic test. |

`TS,TS1,TS.max,TS1.max` |
respectively the test statistic, the likelihood ratio test, the maximum of the test statistic and the maximum likelihood ratio test. |

`window1,window2` |
indices from which the threshold was chosen. |

`Paretodata` |
logical: if TRUE the distribution of the data is a Pareto distribution. |

`Paretotail` |
logical: if TRUE a Pareto tail was detected. |

`madapt` |
the first indice of the TestingGrid for which the test statistic exceeds the critical value. |

`kadapt` |
the adaptive indice of the threshold. |

`kadapt.maxlik` |
the maximum likelihood corresponding to the adaptive threshold in the selected testing grid. |

`hadapt` |
the adaptive weighted parameter of the Pareto distribution after the threshold. |

`Xadapt` |
the adaptive threshold. |

Ion Grama, Kevin Jaunatre

Grama, I. and Jaunatre, K. (2018). Estimation of Extreme Survival Probabilities with Cox Model. arXiv:1805.01638.

library(survival) data(bladder) X <- bladder2$stop-bladder2$start Z <- as.matrix(bladder2[, c(2:4, 8)]) delta <- bladder2$event ord <- order(X) X <- X[ord] Z <- Z[ord,] delta <- delta[ord] cph<-coxph(Surv(X, delta) ~ Z) ca <- cox.adapt(X, cph, delta, Z)

[Package *extremefit* version 1.0.2 Index]