Computes the maximum likelihood estimator, the smoothed maximum likelihood
estimator and pointwise bootstrap confidence intervals for the distribution
function under current status data.
Groeneboom and Hendrickx (2017)
The goal of curstatCI is to obtain confidence intervals for the distribution function of a random variable based on current status data. In the current status model, the variable of interest X with distribution function F0 is not observed directly. A censoring variable T is observed instead together with the indicator Δ = (X ≤ T). curstatCI provides functions to estimate the distribution function F0 and to construct pointswise confidence intervals around F0(t) based on an observed sample (T1, Δ1),…,(Tn, Δn) of size n from the observable random vector (T, Δ).
You can install curstatCI from CRAN with:
The package curstatCI requires the library Rcpp. To use the functions available in curstatCI load:
load(Rcpp)load(curstatCI)
You can install curstatCI from github with:
# install.packages("devtools")devtools::install_github("kimhendrickx/curstatCI")
This is a basic example which shows you how to obtain the confidence intervals for the distribution function of the time to infection for the Rubella data set. More information on the data and usage of the package can be found in the vignette "curstatCI":
library(Rcpp)library(curstatCI)set.seed(1)data(rubella)grid <-1:80bw <-ComputeBW(data=rubella, x=grid)#> The computations took 1.256 secondsout<-ComputeConfIntervals(data=rubella,x=grid,alpha=0.05, bw = bw)#> The program produces the Studentized nonparametric bootstrap confidence intervals for the cdf, using the SMLE.#>#> Number of unique observations: 225#> Sample size n = 230#> Number of Studentized Intervals = 80#> Number of Non-Studentized Intervals = 0#> The computations took 0.414 secondsout$MLE#> [,1] [,2]#> [1,] 0.0000 0.0000000#> [2,] 0.9452 0.2000000#> [3,] 1.2301 0.4857143#> [4,] 5.6411 0.5000000#> [5,] 9.4603 0.5714286#> [6,] 12.4548 0.8571429#> [7,] 15.4466 0.8666667#> [8,] 20.8000 0.8750000#> [9,] 23.2219 0.9285714#> [10,] 25.2932 0.9401709#> [11,] 77.8027 1.0000000smle <- out$SMLEleft<-out$CI[,1]right<-out$CI[,2]ConfInt<-cbind(smle, left, right)head(ConfInt)#> smle left right#> [1,] 0.05205647 0.04645370 0.05684488#> [2,] 0.10387679 0.09062863 0.11518718#> [3,] 0.15522742 0.13450756 0.17291065#> [4,] 0.20587997 0.17795836 0.22970949#> [5,] 0.25561365 0.22085116 0.28528225#> [6,] 0.30421750 0.26290425 0.33934792