That's much more fair I failed to note that extra line of work. Good to know looping can be avoided. Thanks!
-----Original Message----- From: arun [mailto:smartpink...@yahoo.com] Sent: August 20, 2013 11:11 AM To: Folkes, Michael Cc: R help Subject: Re: function on columns of two arrays Hi Michael, I run it on my system after deleting one of the lines from method1 (as it was not necessary) method1 <- function(){ a1<- data.frame(a) b1<- data.frame(b) lapply(seq_len(ncol(a1)),function(i) summary(lm(b1[,i]~a1[,i]))$coef) } linRegFun<- function(x,y){ res<- summary(lm(y~x))$coef} method4 <- function(){ a1<- data.frame(a) b1<- data.frame(b) mapply(linRegFun,a1,b1) } system.time( method1() ) # user system elapsed # 67.504 0.008 67.636 system.time(method2()) # user system elapsed # 86.952 0.292 87.408 system.time(method3()) # user system elapsed # 50.856 0.000 50.948 system.time(method4()) # user system elapsed # 46.444 0.000 46.526 A.K. ----- Original Message ----- From: "Folkes, Michael" <michael.fol...@dfo-mpo.gc.ca> To: "Law, Jason" <jason....@portlandoregon.gov>; arun <smartpink...@yahoo.com> Cc: R help <r-help@r-project.org> Sent: Tuesday, August 20, 2013 1:57 PM Subject: RE: function on columns of two arrays Here's a tiny summary of the speed results using different methods to run lm on common columns from two arrays. Sadly looping is fastest. I don't know if any are sensitive to array dimensions (more columns, fewer layers etc). I invite correction, or suggestions for improvement to avoid looping. Going on vacation, so I won't reply until next Wednesday. Thanks! Michael ##### begin R script #shows three ways to do lm on common columns from two arrays. #sadly looping is fastest by a long shot a <- array(1:60,dim=c(20,20,2000)) b <- a*3+10 #method 1 # arun [smartpink...@yahoo.com] method1 <- function(){ a1<- data.frame(a) b1<- data.frame(b) lapply(seq_len(ncol(a1)),function(i) lm(b1[,i]~a1[,i])) lapply(seq_len(ncol(a1)),function(i) summary(lm(b1[,i]~a1[,i]))$coef) } #method 2 #Jason Law Statistician City of Portland method2 <- function(){ library(abind) library(plyr) c <- abind(a,b, along = 4) results <- alply(c, c(2,3), function(x) lm(x[,2] ~ x[,1])) ldply(results, function(x) summary(x)$coef) } #method3 #looping method3 <- function(){ results <- matrix(NA,ncol=4,nrow=2*dim(a)[2]*dim(a)[3]) counter <- 1 for(layer in 1:dim(a)[3]){ for(col.val in 1:dim(a)[2]){ results[counter:(counter+1),] <- summary(lm(b[,col.val,layer]~a[,col.val,layer]))$coef counter <- counter+2 } } }#END method3 # system.time( method1() ) # system.time( method2() ) # system.time( method3() ) # # > system.time( method1() ) # user system elapsed # 210.52 0.09 212.03 # # > system.time( method2() ) # user system elapsed # 123.52 0.13 124.07 # # > system.time( method3() ) # user system elapsed # 79.07 0.01 79.23 ______________________________________________ R-help@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.
