From: Mayeul KAUFFMANN <mayeul.kauffmann_at_tiscali.fr>

Date: Tue 27 Jul 2004 - 08:28:45 EST

+cluster(id)

,data=x,robust=T))

rbind(z,c(a1,a2,a3,coxtmp$wald.test, coxtmp$rscore, coxtmp$loglik, coxtmp$score))->z

}

z <- data.frame(z)

names(z) <- c("a1","a2", "a3","wald.test", "rscore",

R-help@stat.math.ethz.ch mailing list

https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Jul 27 08:37:35 2004

Date: Tue 27 Jul 2004 - 08:28:45 EST

>In the case of recurrent events coxph() is not

*> using maximum likelihood or even maximum partial likelihood. It is
**> maximising the quantity that (roughly speaking) would be the partial
**> likelihood if the covariates explained all the cluster differences.
*

I could have non repeating events by removing countries once they have experienced a war. But I'm not sure it will change the estimation procedure since this will change the dataset only, not the formula coxph(Surv(start,stop,status)~x1+x2+...+cluster(id),robust=T)

I am not sure I understood you well: do you really mean "recurrent events" alone or "any counting process notation (including allowing for recurrent events)".

>In the case of recurrent events coxph() is not

*> using maximum likelihood or even maximum partial likelihood.
*

Then, what does fit$loglik give in this case? Still a likelihood or a
valid criterion to maximise ?

If not, how to get ("manually") the criterion that was maximsed?

That's of interest for me since

> I created artificial covariates measuring the proximity since some

events: exp(-days.since.event/a.chosen.parameter).

...and I used fit$loglik to chose a.chosen.parameter from 8 values, for 3 types of events:

la<-c(263.5, 526.9,1053.9,2107.8,4215.6,8431.1) #list of values to choose
from

z<-NULL;for(a1 in la) for(a2 in la) for(a3 in la) {coxtmp <-

(coxph(Surv(start,stop,status)~ +I(exp(-days.since.event.of.type.one/a1)) +I(exp(-days.since.event.of.type.two/a2)) +I(exp(-days.since.event.of.type.three/a3))+ other.time.dependent.covariates

+cluster(id)

,data=x,robust=T))

rbind(z,c(a1,a2,a3,coxtmp$wald.test, coxtmp$rscore, coxtmp$loglik, coxtmp$score))->z

}

z <- data.frame(z)

names(z) <- c("a1","a2", "a3","wald.test", "rscore",

"NULLloglik","loglik", "score") z[which.max(z$rscore),] z[which.max(z$loglik),]

The last two commands gave me almost always the same set for c(a1,a2,a3). But they sometimes differed significantly on some models.

Which criteria (if any ?!) should I use to select the best set c(a1,a2,a3) ?

(If you wish to see what the proximity variables look like, run the
following code. The dashed lines show the "half life" of the proximity
variable,here=6 months, which is determined by a.chosen.parameter, e.g.
a1=la[1]:

#start of code

curve(exp(-(x)/263.5),0,8*365.25,xlab="number of days since last political
regime change (dsrc)",ylab="Proximity of political regime change =
exp(-dsrc/263.5)",las=1)

axis(1,at=365.25/2, labels= "(6 months)");axis(2,at=seq(0,1,.1),las=1)
lines(c(365.25/2,365.25/2,-110),c(-.05,0.5,0.5),lty="dashed")
#end of code)

Thanks a lot again.

Mayeul KAUFFMANN

Univ. Pierre Mendes France

Grenoble - France

R-help@stat.math.ethz.ch mailing list

https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Jul 27 08:37:35 2004

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