[R] Loop avoidance in simulating a vector

[Bert Gunter]

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: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Christos Hatzis
Sent: Thursday, October 16, 2008 1:06 PM
To: 'David Afshartous'; r-help at r-project.org
Subject: Re: [R] Loop avoidance in simulating a vector

Have a look at mapply.

-Christos 

> -----Original Message-----
> From: r-help-bounces at r-project.org 
> [mailto:r-help-bounces at r-project.org] On Behalf Of David Afshartous
> Sent: Thursday, October 16, 2008 3:47 PM
> To: r-help at 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
> 
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>

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