use Rprof to find where time is being spent. probably in 'plot' which might
imply it is not the 'for' loop and therefore beyond your control.
Sent from my iPad
On Feb 25, 2011, at 6:19, Ivan Calandra <ivan.calan...@uni-hamburg.de> wrote:
> Thanks Nick for your quick answer.
> It does work (no missed bracket!) but unfortunately doesn't really speed up
> anything: with my real data, it takes 82.78 seconds with the double lapply()
> instead of 83.59s with the double loop (about 0.8 s).
>
> It looks like my double loop was not that bad. Does anyone know another
> faster way to do this?
>
> Thanks again in advance,
> Ivan
>
> Le 2/25/2011 11:41, Nick Sabbe a écrit :
>> Simply avoiding the for loops by using lapply (I may have missed a bracket
>> here or there cause I did this without opening R)...
>> Haven't checked the speed up, though.
>>
>> lapply(seq.yvar, function(k){
>> plot(mydata1[[k]]~mydata1[[ind.xvar]], type="p",
>> xlab=names(mydata1)[ind.xvar], ylab=names(mydata1)[k])
>> lapply(seq_along(mydata_list), function(j){
>> foo_reg(dat=mydata_list[[j]], xvar=ind.xvar, yvar=k, mycol=j,
>> pos=mypos[j], name.dat=names(mydata_list)[j])
>> return(NULL)
>> })
>> invisible(NULL)
>> })
>>
>> HTH,
>>
>> Nick Sabbe
>> --
>> ping: nick.sa...@ugent.be
>> link: http://biomath.ugent.be
>> wink: A1.056, Coupure Links 653, 9000 Gent
>> ring: 09/264.59.36
>>
>> -- Do Not Disapprove
>>
>>
>>
>>
>> -----Original Message-----
>> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
>> Behalf Of Ivan Calandra
>> Sent: vrijdag 25 februari 2011 11:20
>> To: r-help
>> Subject: [R] speed up process
>>
>> Dear users,
>>
>> I have a double for loop that does exactly what I want, but is quite
>> slow. It is not so much with this simplified example, but IRL it is slow.
>> Can anyone help me improve it?
>>
>> The data and code for foo_reg() are available at the end of the email; I
>> preferred going directly into the problematic part.
>> Here is the code (I tried to simplify it but I cannot do it too much or
>> else it wouldn't represent my problem). It might also look too complex
>> for what it is intended to do, but my colleagues who are also supposed
>> to use it don't know much about R. So I wrote it so that they don't have
>> to modify the critical parts to run the script for their needs.
>>
>> #column indexes for function
>> ind.xvar<- 2
>> seq.yvar<- 3:4
>> #position vector for legend(), stupid positioning but it doesn't matter here
>> mypos<- c("topleft", "topright","bottomleft")
>>
>> #run the function for columns 3&4 as y (seq.yvar) with column 2 as x
>> (ind.xvar) for all 3 datasets (mydata_list)
>> par(mfrow=c(2,1))
>> for (i in seq_along(seq.yvar)){
>> k<- seq.yvar[i]
>> plot(mydata1[[k]]~mydata1[[ind.xvar]], type="p",
>> xlab=names(mydata1)[ind.xvar], ylab=names(mydata1)[k])
>> for (j in seq_along(mydata_list)){
>> foo_reg(dat=mydata_list[[j]], xvar=ind.xvar, yvar=k, mycol=j,
>> pos=mypos[j], name.dat=names(mydata_list)[j])
>> }
>> }
>>
>> I tried with lapply() or mapply() but couldn't manage to pass the
>> arguments for names() and col= correctly, e.g. for the 2nd loop:
>> lapply(mydata_list, FUN=function(x){foo_reg(dat=x, xvar=ind.xvar,
>> yvar=k, col1=1:3, pos=mypos[1:3], name.dat=names(x)[1:3])})
>> mapply(FUN=function(x) {foo_reg(dat=x, name.dat=names(x)[1:3])},
>> mydata_list, col1=1:3, pos=mypos, MoreArgs=list(xvar=ind.xvar, yvar=k))
>>
>> Thanks in advance for any hints.
>> Ivan
>>
>>
>>
>>
>> #create data (it looks horrible with these datasets but it doesn't
>> matter here)
>> mydata1<- structure(list(species = structure(1:8, .Label = c("alsen",
>> "gogor", "loalb", "mafas", "pacyn", "patro", "poabe", "thgel"), class =
>> "factor"), fruit = c(0.52, 0.45, 0.43, 0.82, 0.35, 0.9, 0.68, 0), Asfc =
>> c(207.463765, 138.5533755, 70.4391735, 160.9742745, 41.455809,
>> 119.155109, 26.241441, 148.337377), Tfv = c(47068.1437773483,
>> 43743.8087431582, 40323.5209129239, 23420.9455581495, 29382.6947428651,
>> 50460.2202192311, 21810.1456510625, 41747.6053810881)), .Names =
>> c("species", "fruit", "Asfc", "Tfv"), row.names = c(NA, 8L), class =
>> "data.frame")
>>
>> mydata2<- mydata1[!(mydata1$species %in% c("thgel","alsen")),]
>> mydata3<- mydata1[!(mydata1$species %in% c("thgel","alsen","poabe")),]
>> mydata_list<- list(mydata1=mydata1, mydata2=mydata2, mydata3=mydata3)
>>
>> #function for regression
>> library(WRS)
>> foo_reg<- function(dat, xvar, yvar, mycol, pos, name.dat){
>> tsts<- tstsreg(dat[[xvar]], dat[[yvar]])
>> tsts_inter<- signif(tsts$coef[1], digits=3)
>> tsts_slope<- signif(tsts$coef[2], digits=3)
>> abline(tsts$coef, lty=1, col=mycol)
>> legend(x=pos, legend=c(paste("TSTS ",name.dat,":
>> Y=",tsts_inter,"+",tsts_slope,"X",sep="")), lty=1, col=mycol)
>> }
>>
>
> --
> Ivan CALANDRA
> PhD Student
> University of Hamburg
> Biozentrum Grindel und Zoologisches Museum
> Abt. Säugetiere
> Martin-Luther-King-Platz 3
> D-20146 Hamburg, GERMANY
> +49(0)40 42838 6231
> ivan.calan...@uni-hamburg.de
>
> **********
> http://www.for771.uni-bonn.de
> http://webapp5.rrz.uni-hamburg.de/mammals/eng/1525_8_1.php
>
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