Compute the extreme quantile procedure for Cox model
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)
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