[R] speed up process

[Ivan Calandra]

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.calandra at uni-hamburg.de

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