cox.adapt {extremefit} | 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.
See Also
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)