ic_np: Non-Parametric Estimator for Interval Censored Data

In icenReg: Regression Models for Interval Censored Data

Non-Parametric Estimator for Interval Censored Data

Description

Fits the non-parametric maximum likelihood estimator (NPMLE) for univariate interval censored data. This is a generalization of the Kaplan-Meier curves that allows for interval censoring. Also referred to as the Turnbull estimator.

Usage

ic_np(
  formula = NULL,
  data,
  maxIter = 1000,
  tol = 10^-10,
  B = c(0, 1),
  weights = NULL
)

Arguments

formula	Formula for stratification. If only one group, can be left blank and data must be entered as n x 2 matrix.
data	A data.frame or an n x 2 matrix. See details.
maxIter	Maximum iterations
tol	Numeric tolerance
B	Should intervals be open or closed? See details.
weights	Weights (optional)

Details

data must be an n x 2 matrix or data.frame containing two columns of data representing left and right sides of the censoring interval, denoted L and R. This allows for left censored (L == 0), right censored (R == inf), uncensored (L == R) along with general interval censored observations.

The argument B determines whether the intervals should be open or closed, i.e. B = c(0,1) implies that the event occurs within the interval (l,u]. The exception is that if l == u, it is assumed that the event is uncensored, regardless of B.

The NPMLE is fit using an efficient implementation of the EMICM algorithm.

Author(s)

Clifford Anderson-Bergman

References

Turnbull, B. (1976) The empricial distribution with arbitrarily grouped and censored data Journal of the Royal Statistical Society B, vol 38 p290-295

Wellner, J. A., and Zhan, Y. (1997) A hybrid algorithm for computation of the maximum likelihood estimator from censored data, Journal of the American Statistical Association, Vol 92, pp945-959

Anderson-Bergman, C. (2016) An efficient implementation of the EMICM algorithm for the interval censored NPMLE Journal of Computational and Graphical Statistics, just accepted

Examples

data(miceData)
fit <- ic_np(cbind(l, u) ~ grp, data = miceData)
# Stratifies fits by group

plot(fit) 

icenReg documentation built on May 29, 2024, 5:32 a.m.