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Ragnhild Sørum

2003-Nov-12 14:26 UTC

[R] repeat until function

Hi,

I'm in this situation:
I what to generate N random numbers(integer) that are different from each 
other.
One suggestion:

tabel <- rep(NULL, N)
for (i in 1:N){
   temp <- as.integer(runif(1,1,max))
   if(temp in tabel) {
       repeat (?) (temp <- as.integer(runif(i,i,max)))
       until (?) ((temp in tabel) ==FALSE)
   }
   else{ tabel[i] <- temp}

I can't use repeat/until - don't exist....
Any suggestions?


Thanks
*Ragnhild*

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MSN Messenger http://www.msn.no/messenger Den korteste veien mellom deg og 
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RBaskin@ahrq.gov

2003-Nov-12 14:42 UTC

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[R] repeat until function

See ?sample which has a default of replace = FALSE.

Usage:

     sample(x, size, replace = FALSE, prob = NULL)
> N<-10
> sample(1:(2*N),N) [1]  1 14 11  3 19  8  4  7 18 17

bob


-----Original Message-----
From: Ragnhild Sørum [mailto:rsoerum80@hotmail.com] 
Sent: Wednesday, November 12, 2003 9:26 AM
To: r-help@stat.math.ethz.ch
Subject: [R] repeat until function

Hi,

I'm in this situation:
I what to generate N random numbers(integer) that are different from each 
other.
One suggestion:

tabel <- rep(NULL, N)
for (i in 1:N){
   temp <- as.integer(runif(1,1,max))
   if(temp in tabel) {
       repeat (?) (temp <- as.integer(runif(i,i,max)))
       until (?) ((temp in tabel) ==FALSE)
   }
   else{ tabel[i] <- temp}

I can't use repeat/until - don't exist....
Any suggestions?


Thanks
*Ragnhild*

_________________________________________________________________
MSN Messenger http://www.msn.no/messenger Den korteste veien mellom deg og 
dine venner

______________________________________________
R-help@stat.math.ethz.ch mailing list
https://www.stat.math.ethz.ch/mailman/listinfo/r-help

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John Fox

2003-Nov-12 14:47 UTC

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[R] repeat until function

Dear Ragnhild,

You can break out of a loop (see ?break), but, if I understand correctly 
what you want, why not just use sample(max, N)?

John

At 02:26 PM 11/12/2003 +0000, Ragnhild S?rum wrote:>Hi,
>
>I'm in this situation:
>I what to generate N random numbers(integer) that are different from each 
>other.
>One suggestion:
>
>tabel <- rep(NULL, N)
>for (i in 1:N){
>   temp <- as.integer(runif(1,1,max))
>   if(temp in tabel) {
>       repeat (?) (temp <- as.integer(runif(i,i,max)))
>       until (?) ((temp in tabel) ==FALSE)
>   }
>   else{ tabel[i] <- temp}
>
>I can't use repeat/until - don't exist....
>Any suggestions?
-----------------------------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario, Canada L8S 4M4
email: jfox at mcmaster.ca
phone: 905-525-9140x23604
web: www.socsci.mcmaster.ca/jfox

Philipp Pagel

2003-Nov-12 15:16 UTC

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[R] repeat until function

> I what to generate N random numbers(integer) that are different from each 
> other.
> One suggestion:
> 
> tabel <- rep(NULL, N)
> for (i in 1:N){
>   temp <- as.integer(runif(1,1,max))
>   if(temp in tabel) {
>       repeat (?) (temp <- as.integer(runif(i,i,max)))
>       until (?) ((temp in tabel) ==FALSE)
>   }
>   else{ tabel[i] <- temp}
> 
> I can't use repeat/until - don't exist....
repeat ... until loops can be rewritten as while loops.


The easiest way is this:

# n unique random numbers between min and max
sample(min:max, n)


But maybe you don't want to generate the array min:max for some reason
(because it is really large?). Then you can do the whole thing yourself:

# another way to the same result
left <- n
tab <- NULL
while (left > 0) {
	new <- as.integer(runif(left, min, max))
	tab <- unique(c(tab, new))
	left <- n - length(tab)	
}

If max-min is really large and n is not much smaller than max-min you
may want to generate more numbers than you actually need so unique()
leaves you with more results and you don't have to iterate so often...

cu
	Philipp

-- 
Dr. Philipp Pagel                                Tel.  +49-89-3187-3675
Institute for Bioinformatics / MIPS              Fax.  +49-89-3187-3585
GSF - National Research Center for Environment and Health
Ingolstaedter Landstrasse 1
85764 Neuherberg, Germany

Liaw, Andy

2003-Nov-12 17:00 UTC

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[R] repeat until function

As others already pointed out, the fast way is to use sample().

What I'd like to add is the following, which I learned from peeking at the C
code underneath sample():  To draw n samples without replacement from 1:N
(N>=n), you only need a loop from 1 to n that used up n random numbers.  The
algorithm is simple, but IMHO very clever:

pop <- 1:N
samp <- rep(NA, n)
soFar <- N
for (i in 1:n) {
    idx <- ceiling(runif(1) * soFar)
    samp[i] <- pop[idx]
    ## swap the soFar-th and idx-th element of pop.
    tmp <- pop[soFar]
    pop[soFar] <- pop[idx]
    pop[idx] <- tmp
    ## decrease the population by 1.
    soFar <- soFar - 1
}

This is obviously not efficient in high-level languages like R, but in terms
of algorithm, it is a lot more efficient than check-and-reject.  IMHO this
should be described in some book, but I have not seen any book describing
it.

Just my $0.02...
Andy
> -----Original Message-----
> From: Ragnhild S?rum [mailto:rsoerum80 at hotmail.com] 
> Sent: Wednesday, November 12, 2003 9:26 AM
> To: r-help at stat.math.ethz.ch
> Subject: [R] repeat until function
> 
> 
> Hi,
> 
> I'm in this situation:
> I what to generate N random numbers(integer) that are 
> different from each 
> other.
> One suggestion:
> 
> tabel <- rep(NULL, N)
> for (i in 1:N){
>    temp <- as.integer(runif(1,1,max))
>    if(temp in tabel) {
>        repeat (?) (temp <- as.integer(runif(i,i,max)))
>        until (?) ((temp in tabel) ==FALSE)
>    }
>    else{ tabel[i] <- temp}
> 
> I can't use repeat/until - don't exist....
> Any suggestions?
> 
> 
> Thanks
> *Ragnhild*
> 
> _________________________________________________________________
> MSN Messenger http://www.msn.no/messenger Den korteste veien 
> mellom deg og 
> dine venner
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list 
> https://www.stat.math.ethz.ch/mailman/listinfo> /r-help
>

Liaw, Andy

2003-Nov-12 22:31 UTC

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[R] repeat until function

> From: Prof Brian Ripley [mailto:ripley at stats.ox.ac.uk] 
> 
> Ripley (1987) Stochastic Simulation, pp.80-1 for one.  I am 
> pretty sure it is Knuth's book, although I don't have that to 
> hand.  I attribute it to 
> Moses & Oakford (1963).
Thanks to Brian, Peter and Duncan for the info.  And I have both Ripley
(1987) and Knuth (1981), just not smart enough to look there...

The shuffling algorithm is described on pp. 139-140 in Knuth's book (vol. 2,
2nd ed.), and in addition to Moses & Oakford, he also referenced R.
Durstenfeld, CACM 7 (1964), 420.

Best,
Andy

 > -- 
> Brian D. Ripley,                  ripley at stats.ox.ac.uk
> Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
> University of Oxford,             Tel:  +44 1865 272861 (self)
> 1 South Parks Road,                     +44 1865 272866 (PA)
> Oxford OX1 3TG, UK                Fax:  +44 1865 272595
> 
>

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