I am working on a project that requires the generation of Bernoulli outcomes. Typically, I would go about this using the built in sample() function like so:

sample(1:0,n,prob=c(p,1-p),replace=TRUE)

This works great and is fast, even for large n. Problem is, I want to generate each sample with its own unique probability. Seems straight forward enough, I just wrapped the function and vectorized to allow the passing of a vector of *p*.

binomial_sampler<-function(p){
return(sample(1:0,1,prob=c(p,1-p)))
}
bs<-Vectorize(binomial_sampler)

Naming this function *bs()* turned out to be rather prophetic. Nevertheless, I can call this function by passing my unique vector of outcome probabilities. And indeed I get the result I’m looking for.

bs(my_p_vec)

Problem is, this turns out to be very slow. It would seem that there is quite a bit of overhead to calling sample() for one sample at a time. R’s RNGs are very fast for generating many iid samples, so I started thinking like my old c++ programming self and tried a different approach.

Nbs<-function(p)
{
U<-runif(length(p),0,1)
outcomes<-U<p
return(U)
}

I call the new version Nbs for “New Bernoulli Sampler”, or “Not Bull Shit”. And what a difference it made indeed!

library(rbenchmark)
p<-runif(1000)
res <- benchmark(bs(p), Nbs(p))
print(res)
test replications elapsed relative user.self sys.self user.child sys.child
2 Nbs(p) 100 0.007 1 0.008 0.000 0 0
1 bs(p) 100 1.099 157 1.080 0.016 0 0

157x faster! Now that’s a speedup to write home about.

Dan “The Man” Bernoulli

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**Tags:** bernoulli trials, C, code optimization, computer, iid, Probability, rbenchmark, RNG, rstats, sampling, Simulation, Software, Uncategorized, uniform random number