Yes, but the difference is that the looping in mapply is done in C. There are no interpreted loops in mapply,as far as I can see.
-Christos > -----Original Message----- > From: Bert Gunter [mailto:[EMAIL PROTECTED] > Sent: Thursday, October 16, 2008 4:13 PM > To: [EMAIL PROTECTED]; 'David Afshartous'; > r-help@r-project.org > Subject: RE: [R] Loop avoidance in simulating a vector > > mapply is still a (disguised) loop (at the interpreted > level). So other than improving code readability (always a > good thing!), it shouldn't make much of an efficiency difference. > > A longer answer is: if all you're doing is a location-scale > family of distributions, then creating a matrix of standard > normal (or whatever) distributed data for all 1:N at once and > then using matrix operations to multiply and add, say, so > each column becomes your different distribution might be > faster. This gets the loops down to C code. > > A shorter answer is: it's unlikely that any of this makes > enough of a difference to be worth the effort. Random number > generation is so efficient in R that "avoiding loops" rarely matters. > > Also see ?replicate for a way to perhaps write cleaner code > (but still using hidden interpreted loops). > > -- Bert Gunter > > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Christos Hatzis > Sent: Thursday, October 16, 2008 1:06 PM > To: 'David Afshartous'; r-help@r-project.org > Subject: Re: [R] Loop avoidance in simulating a vector > > Have a look at mapply. > > -Christos > > > -----Original Message----- > > From: [EMAIL PROTECTED] > > [mailto:[EMAIL PROTECTED] On Behalf Of David Afshartous > > Sent: Thursday, October 16, 2008 3:47 PM > > To: r-help@r-project.org > > Subject: [R] Loop avoidance in simulating a vector > > > > > > > > All, > > > > I'd like to simulate a vector that is formed from many distinct > > distributions and avoid a loop if possible. E.g, consider: > > > > mu = c(1, 2, 3) > > sigma = c(1, 2, 3) > > n = c(10, 10, 10) > > > > And we simulate a vector of length 30 that consists of N(mu[i], > > sigma[i]) > > distributed data, each of length n[i]. Of course for just > > three groups we > > can simply write it out as: > > > > DV = c(rnorm(n[1], mu[1], sigma[1]), rnorm(n[2], mu[2], sigma[2]), > > rnorm(n[3], mu[3], sigma[3]) ) > > > > For many groups we can use a loop (assuming equal numbers > per group): > > > > n = n[1] > > DV = numeric(N*n) > > for (i in 1:N) { > > DV[(n*i - (n-1)): (n*i)] = rnorm(n, mu[i], sigma[i]) > > } > > > > Is there any way to do the general cas without using a loop? > > > > Cheers, > > David > > > > ______________________________________________ > > 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. > > > > > > ______________________________________________ > 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. > > > ______________________________________________ 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.