WKME {MAMSE} | R Documentation |
Computes the weighted Kaplan-Meier estimate over some time points with optional confidence intervals.
WKME(x,ub,lb=0,time=NULL,boot=NULL,REP=1000)
x |
A list of m samples. Each element is an n by 2 matrix whose
second column is an indicator of whether the
time in column 1 is observed (1) or censored (0). |
lb,ub |
Lower and upper bounds of the integral of the MAMSE criterion. |
time |
A vector of times at which to compute the Kaplan-Meier estimate. |
boot |
When NULL, bootstrap confidence intervals are not generated. Otherwise must be a number in (0,1) corresponding to the coverage probability of the bootstrap intervals to be built. |
REP |
When bootstrap is used, controls the number of pseudo-sample to generate. |
This function calculates the weighted Kaplan-Meier estimate and can provide pointwise bootstrap confidence intervals.
List of elements:
x |
Sorted list of the times (observed and censored) from each samples |
weight |
The size of the jump that the Kaplan-Meier estimate allocates to each
time in x . |
time |
Vector of time points where the function is evaluated. |
kme |
The Kaplan-Meier estimate for Population 1 evaluated at time . |
kmeCI |
Pointwise bootstrap confidence interval for kme . |
wkme |
The weighted Kaplan-Meier estimate evaluated at time . |
wkmeCI |
Pointwise bootstrap confidence interval for wkme . |
J.-F. Plante (2007). Adaptive Likelihood Weights and Mixtures of Empirical Distributions. Unpublished doctoral dissertation, University of British Columbia.
J.-F. Plante (2009). About an adaptively weighted Kaplan-Meier estimate. Lifetime Data Analysis. Under revision.
set.seed(2009) x=list( cbind(rexp(20),sample(c(0,1),20,replace=TRUE)), cbind(rexp(50),sample(c(0,1),50,replace=TRUE)), cbind(rexp(200),sample(c(0,1),200,replace=TRUE)) ) allx=pmin(1,c(x[[1]][x[[1]][,2]==1,1],x[[2]][x[[2]][,2]==1,1], x[[3]][x[[3]][,2]==1,1])) K=WKME(x,1,time=sort(unique(c(0,1,allx,allx-.0001))),boot=.9) plot(K$time,K$wkme,type='l',col="blue",xlab="x", ylab="P(X<=x)",ylim=c(0,1)) lines(K$time,K$kme[,1],col="red") lines(K$time,K$wkmeCI[1,],lty=2,col="blue") lines(K$time,K$wkmeCI[2,],lty=2,col="blue") lines(K$time,K$kmeCI[1,],lty=2,col="red") lines(K$time,K$kmeCI[2,],lty=2,col="red") legend(.1,.9,c("Weighted Kaplan-Meier","Kaplan-Meier"), col=c("blue","red"),lty=c(1,1))