cox.adapt: Compute the extreme quantile procedure for Cox model
In extremefit: Estimation of Extreme Conditional Quantiles and Probabilities

cox.adapt

R Documentation

Compute the extreme quantile procedure for Cox model

Description

Compute the extreme quantile procedure for Cox model

Usage

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
)

Arguments

`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.

Details

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.

Value

`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.

Author(s)

Ion Grama, Kevin Jaunatre

References

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

Examples


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)

extremefit documentation built on Aug. 21, 2025, 6:03 p.m.