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
--
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-- 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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______________________________________________
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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.
