For each of a series of values for the cure parameter p0 run the function logcon
and evaluate the (normalized) loglikelihood at (phi,p_0), where phi is the log subprobability density returned by logcon
. This serves for (approximate) joint likelihood maximization in (phi,p0).
1 2  cure.profile(x, p0grid=seq(0,0.95,0.05), knot.prec=IQR(x[x<Inf])/75,
reduce=TRUE, control=lc.control())

x 
a twocolumn matrix of n >= 2 rows containing the data intervals. 
p0grid 
a vector of values p_0 for which the profile loglikelihood is to be evaluated. 
knot.prec, reduce, control 
arguments passed to the function 
A list containing the following values:
p0hat 
the element in 
status 
the vector of (normalized) profile loglikelihood values for the elements of 
For a large p0grid
vector (fine grid) computations may take a long time. Consider using the option adapt.p0
in the function logcon
for a much faster method of joint likelihood maximization in (phi,p0).
Dominic Schuhmacher dominic.schuhmacher@mathematik.unigoettingen.de
Kaspar Rufibach kaspar.rufibach@gmail.com
Lutz Duembgen duembgen@stat.unibe.ch
logcon
, loglike
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## The example from the logconcenspackage help page:
set.seed(11)
x < rgamma(50,3,1)
x < cbind(x,ifelse(rexp(50,1/3) < x,Inf,x))
## Not run:
plotint(x)
progrid < seq(0.1,0.6,0.025)
prores < cure.profile(x, progrid)
plot(progrid, prores$loglike)
prores$p0hat
res < logcon(x, p0=prores$p0hat)
plot(res, type="survival")
## End(Not run)

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