For each of a series of values for the cure parameter *p0* run the function `logcon`

and evaluate the (normalized) log-likelihood 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 two-column matrix of |

`p0grid` |
a vector of values |

`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 log-likelihood 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.uni-goettingen.de

Kaspar Rufibach kaspar.rufibach@gmail.com

Lutz Duembgen duembgen@stat.unibe.ch

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
## The example from the logconcens-package 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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