If you want to avoid using a loop, rather than coding a loop, then someting like:
DV <- rnorm(sum(n)) DV <- DV * rep(sigma, times=n) + rep(mu, times=n) will achieve the same as the loop you specified, but generalised to non-constant n. HTH Ray Brownrigg On Fri, 17 Oct 2008, Bert Gunter wrote: > 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. ______________________________________________ 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.
