how to make simulation faster
The code you posted was not runnable. 'r' and at least 'Zi' were missing. The 'total time' is the amount of the elapsed time that it was sampling with the given function. The "self time" is how much time was actually spent in that function.
From your data, one of the big hitter is the "factor" function. This
is probably within the "aggregate" function since this is where most of the "total time" is being spent. You may want to look at what you are trying to do with the "aggregate" and see if there is another way of doing it. You seem to have an object "i" being used in the aggregate that does not seem to be defined. There are probably other ways of speeding it up. Here is one that compares 'aggregate' to 'sapply' to do the same thing:
x <- as.numeric(1:1e6) z <- sample(100, 1e6, TRUE) system.time(r1 <- aggregate(x, list(z), sum))
user system elapsed 3.62 0.05 3.67
system.time(r2 <- sapply(split(x, z), sum))
user system elapsed 0.56 0.00 0.56 So this is about a 6X increase in performance.
On Fri, Oct 26, 2012 at 9:08 AM, stats12 <skarmv at gmail.com> wrote:
Hi,
Thank you for your reply. I updated my post with the code. Also, about
posting from Nabble, since I am a new user I didn't know about that problem.
If I post to the mailing list ( r-help at r-project.org), would it get rid of
that problem?
output1<-vector("numeric",length(1:r))
output2<-vector("numeric",length(1:r))
output3<-vector("numeric",length(1:r))
output4<-vector("numeric",length(1:r))
for (m in 1:r){
ll<-function(p){
cumhaz<-(time*exp(Zi*p[3]))*p[1]
cumhaz<-aggregate(cumhaz,by=list(i),FUN=sum)
lnhaz<-delta*log(exp(Zi*p[3])*p[1])
lnhaz<-aggregate(lnhaz,by=list(i),FUN=sum)
lnhaz<-lnhaz[2:(r+1)]
cumhaz<-cumhaz[2:(r+1)]
lik<-r[m]*log(p[2])-
sum((di[,m]+1/p[2])*log(1+cumhaz[,m]*p[2]))+sum(lnhaz[,m])-
n*log(gamma(1/p[2]))+sum(log(gamma(di[,m]+1/p[2])))
-lik
}
initial<-c(1,0.5,0.5)
estt<-nlm(ll,initial,hessian=T)
lambda<-estt$estimate[1]
theta<-estt$estimate[2]
beta<-estt$estimate[3]
hessian<-t$hessian
cov<-solve(hessian)
vtheta<-cov[2,2]
vbeta<-cov[3,3]
output1[m]<-theta
output2[m]<-beta
output3[m]<-vtheta
output4[m]<-vbeta
}
theta<-as.vector(output1)
beta<-as.vector(output2)
vtheta<-as.vector(output3)
vbeta<-as.vector(output4)
Code would be nice: I struggle to think of an basic MLE fitting that
would take ~36s per iteration and scale so badly if you are writing
idiomatic R:
http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example
Finally, I note you're posting from Nabble. Please include context in
your reply -- I don't believe Nabble does this automatically, so
you'll need to manually include it. Most of the regular respondents on
this list don't use Nabble -- it is a _mailing list_ after all -- so
we don't get the forum view you do, only emails of the individual
posts. Combine that with the high volume of posts, and it's quite
difficult to trace a discussion if we all don't make sure to include
context.
--
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______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Jim Holtman Data Munger Guru What is the problem that you are trying to solve? Tell me what you want to do, not how you want to do it.