Teds reply is a bit comforting and as indicated in my post, I am resorting to
using "sample" but as an academic issue, does randomness preclude precision?
Randomness should be in the sequence of zeros and ones and how they are
simulated at each iteration of the process but not in the eventual nature of
the distribution.
I mean if I simulated a Normal (0, 1) and got a Normal(1.5, 2) these would be
very different distributions. It is the same with simulating a Binomial(1,
p=0.15) and getting Binomial(1, 0.154)
[EMAIL PROTECTED] wrote: On 28-May-08 12:53:26, 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.
The difference (77 - 75 =2) is well within the likely sampling
variation when 500 values are sampled independently with
P(1)=0.15:
The standard deviation of the resulting number of 1s is
sqrt(500*0.15*0.85) = 7.98, so the difference of 2 is only 1/4 of
a standard deviation, hence very likely to be equalled or exceeded.
Your chance of getting exactly 75 by this method is quite small:
dbinom(75,500,0.15)
[1] 0.04990852
and your chance of being 2 or more off your target is
1 - sum(dbinom((74:76),500,0.15))
[1] 0.8510483
> Is there another way beyond using "sample" and "rep" together?
It looks as though you are seeking to obtain exactly 75 1s,
randomly situated, the rest being 0s, so in effect you do need
to do something on the lines of "sample" and "rep". Hence,
something like
status <- rep(0,500)
status[sample((1:500),75,replace=FALSE)] <- 1
Hoping this helps,
Ted.
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Date: 28-May-08 Time: 14:19:24
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