Dear Haris, My timings were on a 3 GHz Pentium 4 system with 1 GB of memory running Win XP SP2 and R 2.4.1.
I'm no expert on these matters, and I wouldn't have been surprised by qualitatively different results on different systems, but this difference is larger than I would have expected. One thing that seems particularly striking in your results is the large difference between elapsed time and user CPU time, making me wonder what else was going on when you ran these examples. Regards, John -------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox -------------------------------- > -----Original Message----- > From: Charilaos Skiadas [mailto:[EMAIL PROTECTED] > Sent: Monday, January 22, 2007 10:00 AM > To: John Fox > Cc: r-help@stat.math.ethz.ch; 'miraceti' > Subject: Re: [R] efficient code. how to reduce running time? > > On Jan 21, 2007, at 8:11 PM, John Fox wrote: > > > Dear Haris, > > > > Using lapply() et al. may produce cleaner code, but it won't > > necessarily speed up a computation. For example: > > > >> X <- data.frame(matrix(rnorm(1000*1000), 1000, 1000)) y <- > >> rnorm(1000) > >> > >> mods <- as.list(1:1000) > >> system.time(for (i in 1:1000) mods[[i]] <- lm(y ~ X[,i])) > > [1] 40.53 0.05 40.61 NA NA > >> > >> system.time(mods <- lapply(as.list(X), function(x) lm(y ~ x))) > > [1] 53.29 0.37 53.94 NA NA > > > Interesting, in my system the results are quite different: > > > system.time(for (i in 1:1000) mods[[i]] <- lm(y ~ X[,i])) > [1] 192.035 12.601 797.094 0.000 0.000 > > system.time(mods <- lapply(as.list(X), function(x) lm(y ~ x))) > [1] 59.913 9.918 289.030 0.000 0.000 > > Regular MacOSX install with ~760MB memory. > > > In cases such as this, I don't even find the code using *apply() > > easier to read. > > > > Regards, > > John > > Haris > > ______________________________________________ R-help@stat.math.ethz.ch 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.
