You asked for each of 500 to be included with probability 0.15, not for
15% of 500. If you want the latter, use sample, e.g.
sample(c(rep(1,75), rep(0,425)))
And to see if your 77 is reasonable for binomial sampling:
binom.test(77, 500, 0.15)
Exact binomial test
data: 77 and 500
number of successes = 77, number of trials = 500, p-value = 0.8022
alternative hypothesis: true probability of success is not equal to 0.15
95 percent confidence interval:
0.1234860 0.1886725
sample estimates:
probability of success
0.154
so it certainly is.
On Wed, 28 May 2008, Philip Twumasi-Ankrah wrote:
I am trying to simulate a series of ones and zeros (1 or 0) and I am using
"rbinom" but realizing that the number of successes expected is not accurate.
Any advice out there.
This is the example:
N<-500
status<-rbinom(N, 1, prob = 0.15)
count<-sum(status)
15 percent of 500 should be 75 but what I obtain from the "count" variable is
77 that gives the probability of success to be 0.154. Not very good.
Is there another way beyond using "sample" and "rep" together?
[...]
--
Brian D. Ripley, [EMAIL PROTECTED]
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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