Randomly selecting values from a zero inflated distribution in R

Hello and thanks in advance for the help!

A while back I asked a question about randomly selecting values according to a probability distribution. This is related, but I think it deserves its own post.

The vector I created in the last question was binary, now I would like to generate a weighted vector (ie with bounded integers). I am sampling from a zero-inflated or quasi-poisson distribution with a long tail, so there is a much higher probability of selecting a zero than another value, but there is a finite probability of selecting a large value (eg开发者_如何学C 63).

I can use rpois to select values from a poisson distribution and create a vector of a given length. This is similar to what I would like to do, so I will use it as an example.

e=seq(0:63)
vec<-c(0,0,0,1,1,1)
ones <- which(vec == 1L)
temp=rpois((sum(vec)),e)
vec[ones]<-temp

This works well for assigning a specific number of values selected from a poisson distribution to a vector. Is there anyway to make it quasi-poisson or zero inflated?

There's a big list of the different distributions here: http://cran.r-project.org/web/views/Distributions.html

For zero inflated poisson...

install.packages("gamlss.dist")
library(gamlss.dist)

rZIP(n, mu, sigma)

For quasi-poisson, it looks like there are some capabilities within the VGAM package with quasipoissonff, but that seems to be for fitting rather than generating. It looks like Arthur Charpentier was on to something here - but you really need to know what you're looking for to get the distribution right: http://freakonometrics.blog.free.fr/index.php?post/2010/10/21/How-to-genrerate-variables-from-a-quasi-Poisson-distribution