This code does not do what you describe: it samples one at a time.
It would be much better to take a larger sample (and you can use the
theory to work out how much bigger) and just use x[x > 0] on that. If
that is not big enough, add some more, and if it is too big, keep the
first n.
On Fri, 17 Feb 2006, Chris O'Brien wrote:
> Dear R users,
> I'm wanting to sample from the negative binomial distribution using the
> rnegbin function from the MASS library to create artificial samples for the
> purpose of doing some power calculations. However, I would like to work
> with samples that come from a negative binomial distribution that includes
> only values greater than or equal to 1 (a truncated negative binomial), and
> I can't think of a straightforward way to accomplish this using rnegbin.
>
> One suggestion I've received is that I use an iterative process to select
> numbers from the negative binomial, throw out the zeros, test the
> distribution for the desired parameters, and then repeat until the
> parameters are in an acceptable range. I could then sample this
> 'population' using the sample function, and go from there. One major
> problem with this approach is that it is very time consuming on a desktop
> machine.
>
> Here's a piece of code (from a friend, and untested) that will do such a
> thing:
>
> #
> # Function to generate a vector containing values from a truncated
> # negative binomial distribution (i.e., no zeros). Select desired
> # mean and variance, sample size, initial values for mean and theta,
> # and a threshold value for tests.
> #
> # format: out<-nbin(desired_mean, desired_variance, n, initial_mean,
> # theta, threshold_level)
> #
> # example: out<-nbin(2, 1, 100, 2, 2, 0.1)
> #
> #
> nbin<-function(mu.s,var.s,n,mu.i,theta,test)
> {
> library(MASS)
>
> mu<-0
> var<-0
> rand<-rep(0,n)
> while(abs(mu.s-mu)>=test & abs(var.s-var)>=test)
> {
> for(i in 1:n)
> {
> rnb<-0
> while(rnb==0)
> rnb<-rnegbin(1,mu.i,theta)
> rand[i]<-rnb
> }
> mu<-mean(rand)
> var<-var(rand)
> }
> return(rand)
> }
>
>
>
> As I am wanting to use these samples to compute power for a bootstrap
> procedure, the time demands will become prohibitive very quickly,
> especially for large sample sizes.
> I'm thinking that there must be a more efficient, elegant, and quicker
> solution to the problem, but am having problems coming up with the answer.
> I'd greatly welcome any insight into a more efficient method.
>
> thanks in advance for any insight,
> Chris O'Brien
>
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--
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