PGM {Icens}R Documentation

An implementation of the projected gradient methods for finding the NPMLE.

Description

An estimate of the NPMLE is obtained by using projected gradient methods. This method is a special case of the methods described in Wu (1978).

Usage

PGM(A, pvec, maxiter = 500, tol=1e-07, told=2e-05, tolbis=1e-08,
    keepiter=FALSE)

Arguments

A A is either the m by n clique matrix or the n by 2 matrix containing the left and right end points for each event time.
pvec An initial estimate of the probability vector.
maxiter The maximum number of iterations to take.
tol The tolerance for decreases in likelihood.
told told does not seem to be used.
tolbis The tolerance used in the bisection code.
keepiter A boolean indicating whether to return the number of iterations.

Details

New directions are selected by the projected gradient method. The new optimal pvec is obtained using the bisection algorithm, moving in the selected direction. Convergence requires both the L_1 distance for the improved pvec and the change in likelihood to be below tol.

Value

An object of class icsurv containing the following components:

pf The NPMLE of pvec.
sigma The cummulative sum of pvec.
lval The value of the log likelihood at pvec.
clmat The clique matrix.
method The method used, currently only "MPGM" is possible.
lastchange The difference between pf and the previous iterate.
numiter The number of iterations carried out.
eps The tolerances used.
converge A boolean indicating whether convergence occurred within maxiter iterations.
iter If keepiter is true then this is a matrix containing all iterations - useful for debugging.

Author(s)

Alain Vandal and Robert Gentleman.

References

Some Algorithmic Aspects of the Theory of Optimal Designs, C.–F. Wu, 1978, Annals.

See Also

VEM, ISDM, EMICM, PGM, EM

Examples

    data(cosmesis)
    csub1 <- subset(cosmesis, subset=Trt==0, select=c(L,R))
    PGM(csub1)
    data(pruitt)
    PGM(pruitt)

[Package Icens version 0.5 Index]