icfit(L, R, initp = NA, minerror = 1e-06, maxcount = 1000)
L
and R
combined. If initp
= NA (default) then an initial
value is estimated from the data.error
< minerror
, where
error is the maximum of the reduced gradients (see Gentleman
and Geyer, 1994). Default = 1e-06.u
the Kuhn-Tucker conditions for
convergence are not met. If this happens a warning
will result.error
< minerror
and all values of u
are nonnegative,
otherwise a warning results.sort(unique(c(0,L,R,Inf)))
without the Inf. The output for p
keeps the value
related to Inf so that p
may be inserted into initp
for another run. The outputs for p
and surv
act as if
the jumps in the survival curve happen at the largest
of the possible times (see Gentleman and Geyer, 1994,
Table 2, for a more accurate way to present p
).
Aragon, J. and Eberly, D. (1992), "On Convergence of Convex Minorant Algorithms for Distribution Estimation with Interval-Censored Data," Journal of Computational and Graphical Statistics. 1: 129-140.
Gentleman, R. and Geyer, C. J. (1994), "Maximum Likelihood for Interval Censored Data: Consistency and Computation," Biometrika, 81, 618-623.
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.
plekm
, qq.lnorm
# Calculate and plot a Kaplan-Meier type curve for interval censored data.
# This is S(x) = 1 - F(x) and is the sample estimate of the probability
# of exceeding x. The filmbadge data is used as an example.
data(filmbadge)
out <- icfit(filmbadge$dlow,filmbadge$dhigh)
icplot(out$surv, out$time,XLAB="Dose",YLAB="Exceedance Probability")
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