pleicf {STAND}

Product Limit Estimate for Interval Censored Data

Description

Compute Product Limit Estimate (PLE) of F(x) for interval censored data (i.e., the nonparametric maximum likelihood estimate that generalizes the Kaplan-Meier estimate to interval censored data).

Usage

pleicf(dd,nondet=TRUE,mine=1e-06,maxc=10000,eps=1e-14)

Arguments

dd

n by 2 matrix or data frame (see note below)

nondet

if TRUE, dd is left censored data

mine

minimum error for convergence in icfit. Default = 1e-06.

maxc

maximum number of iterations. Default is 10000.

eps

adjustment factor described by Ng. Default is 1e-14.

Details

This function is a driver function for icfit that uses an EM-algorithm applied to interval censored data (see Turnbull, 1976).

Value

Data frame with columns

a

value of jth uncensored value (ordered)

ple

PLE of F(x) at a

surv

1 - F(), i.e the "survival" or "exceedance" function

prob

prob[X = x] from icfit

n

sample size

Note

If nondet is TRUE column 1 of dd is the data value and column 2 is 1 if a detect and 0 otherwise. If nondet is FALSE dd contains the left and right endpoints required by icfit.

Author(s)

E. L. Frome

References

Fay, M. P. (1999), "Comparing Several Score Tests for Interval Censored Data," Statistics in Medicine,18:273-85. (Corr: 1999, Vol 19, p.2681).

Ng, M. P. (2002), "A Modification of Peto's Nonparametric Estimation of Survival Curves for Interval-Censored Data," Biometrics, 58, 439-442.

Turnbull, B. W. (1976), "The Empirical Distribution Function with Arbitrarily Grouped, Censored and Truncated Data," Journal of the Royal Statistical Society, Series B (Methodological), 38(3), 290-295.

See Also

icfit, plend, plekm

Examples

# PLE for interval censored filmbadge data
data(filmbadge)
ple.fb<-pleicf(filmbadge[,1:2],FALSE) # PLE for input to qq.lnorm
tmp <- qq.lnorm(ple.fb) ; GM<-round(exp(tmp$par[1]));GSD<-round(exp(tmp$par[2]),2)
tp<-paste("Lognormal Q-Q plot for Filmbadge Data GM= ",GM,"GSD= ",GSD)
title(tp) # title for q-q plot with graphical parameter estimates