Uwe,
I am unfortunately not able to upgrade to R 2.15.2 right now, but I have
seen a similar problem with several older R versions. If you want to
test with a shorter script, you can try the lines below. These provoke a
crash from a fresh R session on my machine (R 2.15.1 Windows 7):
Rprof()
z = 1
for (i in 1:1e8) z = z+1/i
This runs without problems without the call to Rprof(). I was far from
using all my memory with this call, but you could also check with a
higher number of loops in case you can run these lines and it is somehow
memory related. Just for comparison, I tried 1e9 loops after a call to
Rprof() on one of our Linux servers (R 2.14.0) without any problems.
Jon
On 12-Dec-12 17:06, Uwe Ligges wrote:
On 12.12.2012 00:05, Marian Talbert wrote:
I'm trying to use Rprof() to identify bottlenecks and speed up a
particullary
slow section of code which reads in a portion of a tif file and compares
each of the values to values of predictors used for model fitting. I've
written up an example that anyone can run. Generally temp would be a
section of a tif read into a data.frame and used later for other
processing.
The first portion which just records the time works in about 6
seconds the
second part causes RGui to immediately close with no error or
warning. Any
advice on how to get Rprof to work or how to speed up this code would be
greatly appreciated. I'm using Windows 7 (which might be my problem)
and R
version 2.15.0.
The problem is rather the R version: I cannot reproduce errors with a
recent R.
Uwe Ligges
CalcMESS<-function(tiff.entry,pred.vect){
f<-sum(pred.vect<tiff.entry)/length(pred.vect)*100
if(is.na(f)) return(NA)
if(f==0)
return((tiff.entry-min(pred.vect))/(max(pred.vect)-min(pred.vect))*100)
if(0<f & f<=50) return(2*f)
if(50<=f & f<100) return(2*(100-f))
if(f==100)
return((max(pred.vect)-tiff.entry)/(max(pred.vect)-min(pred.vect))*100)
else return(NA)
}
train.dat <-
data.frame(a=runif(200),b=runif(200),c=runif(200),d=runif(200))
temp <-
data.frame(a=runif(130000),b=runif(130000),c=runif(130000),d=runif(130000))
pred.rng<-temp
vnames.final.mod <- names(train.dat)
nvars.final <- length(vnames.final.mod)
start.time<-Sys.time()
for(k in 1:nvars.final){
pred.range<-train.dat[,match(vnames.final.mod[k],names(train.dat))]
pred.rng[,k]<-mapply(CalcMESS,tiff.entry=temp[,match(vnames.final.mod[k],names(temp))],MoreArgs=list(pred.vect=pred.range))
}
Sys.time()-start.time
Rprof("C:\\temp\\mapply.out")
for(k in 1:nvars.final){
pred.range<-train.dat[,match(vnames.final.mod[k],names(train.dat))]
pred.rng[,k]<-mapply(CalcMESS,tiff.entry=temp[,match(vnames.final.mod[k],names(temp))],MoreArgs=list(pred.vect=pred.range))
}
Rprof(NULL)
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PLEASE do read the posting guide
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and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
