curstatCI (version 0.1.1)

ComputeMLE: Maximum Likelihood Estimator

Description

The function ComputeMLE computes the Maximum Likelihood Estimator of the distribution function under current status data.

Usage

ComputeMLE(data)

Arguments

data

Dataframe with three variables:

t

Observation points t sorted in ascending order. All observations need to be positive. The total number of unique observation points equals length(t).

freq1

Frequency of observation t satisfying \(x \le t\). The total number of observations with censoring indicator \(\delta =1\) equals sum(freq1).

freq2

Frequency of observation t. The sample size equals sum(freq2). If no tied observations are present in the data length(t) equals sum(freq2).

Value

Dataframe with two variables :

x

jump locations of the MLE

mle

MLE evaluated at the jump locations

Details

In the current status model, the variable of interest \(X\) with distribution function \(F\) is not observed directly. A censoring variable \(T\) is observed instead together with the indicator \(\Delta = (X \le T)\). ComputeMLE computes the MLE of \(F\) based on a sample of size n <- sum(data$freq2).

References

Groeneboom, P. and Hendrickx, K. (2017). The nonparametric bootstrap for the current status model. Electronic Journal of Statistics 11(2):3446-3848.

See Also

ComputeConfIntervals

Examples

# NOT RUN {
library(Rcpp)
library(curstatCI)

# sample size
n <- 1000

# Uniform data  U(0,2)
set.seed(2)
y <- runif(n,0,2)
t <- runif(n,0,2)
delta <- as.numeric(y <= t)

A<-cbind(t[order(t)], delta[order(t)], rep(1,n))
mle <-ComputeMLE(A)
plot(mle$x, mle$mle,type ='s', ylim=c(0,1), main= "",ylab="",xlab="",las=1)

# }