Hello,
As you know R better it will take you less and less time to get it
right, and almost surely less and less lines of code to do the same
thing. Here's a one liner:
set.seed(1510)
s=numeric(length=100000)
for(i in 1:100000){
pop=(rbeta(n=20,shape1=2,shape2=1))
s[i]=sum(pop)
}
set.seed(1510)
s2 <- replicate(1e5, {sum(rbeta(n=20,shape1=2,shape2=1))})
identical(s, s2) # TRUE
As for your question,
hist(s, breaks="Scott") # Looks normal to me
You can use asymptotic normal theory.
Hope this helps,
Rui Barradas
Em 09-11-2012 17:57, rlcorp escreveu:
I am new to R and learned to program 10 years ago in C++. I am currently
working a project that looks at the distribution of randomly generated beta
values. I take 20 random beta values find their sum, repeat 100000 times.
Here is my code that it took me 4 hours to get
s=numeric(length=100000)
for(i in 1:100000){
pop=(rbeta(n=20,shape1=2,shape2=1))
s[i]=sum(pop)
}
So now I have them all in in vector, I would like to maybe sort or count
them to see how many are less than or equal to 10, but am guessing there is
a density r function that may be easier then that.
--
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