LocalKM function

Kernel-based Local Kaplan-Meier Estimator

This is the local KM estimator customized for this library to run in batch mode. It returns the estimated conditional survival probabilities given a user specified set of covariate names that the survival time depends on, a.k.a $F(T > y_0 \mid x_0).$

More specifically, for uncensored data points, we return (1 - tauhat_func()). If the observed data point is censored, then this function returns value -1 as a flag meaning we cannot .

LocalKM(D, bw, NamesCov)

Arguments

• D: a data.frame with column censor_y, column delta, and additional covaraites.
• bw: the bandwidth parameter
• NamesCov: the vector of column names in data.frame D such that the survival time depends on.

Returns

A vector of estimated conditional survival probability evaluated at the observed actual survival time on the same individual

Examples

GenerateData <- function(n)
{
  x1 <- runif(n, min=-0.5,max=0.5)
  x2 <- runif(n, min=-0.5,max=0.5)
  error <- rnorm(n, sd= 1)
  ph <- exp(-0.5+1*(x1+x2))/(1+exp(-0.5 + 1*(x1+x2)))
  a <- rbinom(n = n, size = 1, prob=ph)
  c <- 1.5 +  + runif(n = n, min=0, max=2)
  cmplt_y <-  pmin(2+x1+x2 +  a*(1 - x1 - x2) +  (0.2 + a*(1+x1+x2)) * error, 4.4)
  censor_y <- pmin(cmplt_y, c)
  delta <- as.numeric(c > cmplt_y)
  return(data.frame(x1=x1,x2=x2,a=a, censor_y = censor_y, delta=delta))
}
n <- 20
D <- GenerateData(n)
mean_hat <- LocalKM(D, 5, c("x1","x2"))