1d:T773,# Kaplan-Meier Estimate Custom implementation of the Kaplan Meier estimate. The major difference with existing implementations is that the user can specify exactly the grid points where the estimate is calculated. The implementation corresponds to $1-\hat H(x)$ of [Hua, Patil and Bagkavos (2018)](https://onlinelibrary.wiley.com/doi/full/10.1111/anzs.12224), and is used mainly for estimation of the censoring distribution. ```r KMest(xin, cens, xout) ``` ## Arguments - `xin`: A vector of data points - `xout`: The point at which the estimates should be calculated. - `cens`: Censoring Indicators. ## Details Calculates the well known Kaplan-Meier estimate $$ 1-\hat H(x) = 1, 0 \leq x \leq X_{(1)} $$ or $$ 1-\hat H(x) = \prod_{i=1}^{k-1} \left ( \frac{n-i+1}{n-i+2} \right )^{1-\delta_{(i)}}, X_{(k-1)} ui))/length(ti)*100, "\n") x1<-pmin(ti,ui) # observed data cen<-rep.int(1, SampleSize) # initialize censoring indicators cen[which(ti>ui)]<-0 # 0's correspond to censored indicators arg1<- KMest(x1, cen, x) plot(x, arg1, type="l") ```

KMest function