EMICM {Icens} | R Documentation |
An implementation of the hybrid EM ICM (Iterative convex minorant) estimator of the distribution function proposed by Wellner and Zahn (1997).
EMICM(A, EMstep=TRUE, ICMstep=TRUE, keepiter=FALSE, tol=1e-07, maxiter=1000)
A |
Either the m by n clique matrix or the n by 2 matrix containing the event time intervals. |
EMstep |
Boolean, indicating whether to take an EM step in the iteration. |
ICMstep |
Boolean, indicating whether to take an ICM step. |
keepiter |
Boolean determining whether to keep the iteration states. |
tol |
The maximal L1 distance between successive estimates before stopping iteration. |
maxiter |
The maximal number of iterations to perform before stopping. |
Lots, and they're complicated too!
An object of class icsurv
containing the following
components:
pf |
The estimated probabilities. |
sigma |
The NPMLE of the survival function on the maximal antichains. |
weights |
The diagonal of the likelihood function's second derivative. |
lastchange |
A vector of differences between the last two iterations. |
numiter |
The total number of iterations performed. |
iter |
Is only present if keepiter is true; states of
sigma during the iteration. |
intmap |
The real representation associated with the
probabilities reported in pf . |
Alain Vandal and Robert Gentleman
A hybrid algorithm for computation of the nonparametric maximum likelihood estimator from censored data, J. A. Wellner and Y. Zhan, 1997, JASA.
data(cosmesis) csub1 <- subset(cosmesis, subset=Trt==0, select=c(L,R)) EMICM(csub1) data(pruitt) EMICM(pruitt)