Compound Poisson and vectorized computations

Yesterday, I was asked how to write a code to generate a compound Poisson variables, i.e. a series of random variables  where  is a counting random variable (here Poisson disributed) and where the ‘s are i.i.d (and independent of ), with the convention  when . I came up with the following algorithm, but I was wondering if it was possible to get a better one…

>  rcpd=function(n,rN,rX){
+  N=rN(n)
+  X=rX(sum(N))
+  I=as.factor(rep(1:n,N))
+  S=tapply(X,I,sum)
+  V=as.numeric(S[as.character(1:n)])
+  V[]=0
+  return(V)}

Here, consider – to illustrate – the case where  and ,

>  rN.P=function(n) rpois(n,5)
>  rX.E=function(n) rexp(n,2)

We can generate a sample

>  S=rcpd(1000,rN=rN.P,rX=rX.E)

and check (using simulation) than 

> mean(S)
[1] 2.547033
> mean(rN.P(1000))*mean(rX.E(1000))
[1] 2.548309

and that 

> var(S)
[1] 2.60393
> mean(rN.P(1000))*var(rX.E(1000))+
+ mean(rX.E(1000))^2*var(rN.P(1000))
[1] 2.621376

If anyone might think of a faster algorithm, I’d be glad to hear about it…