Rejection sampling to draw from distributions

Dear friends,

Please find below the code that I have employed for a rejection sampler 
to draw from asymmetric laplace distributions. I was wondering if this 
code can be written more efficiently? Are there more efficient ways of 
drawing random numbers from asymmetric laplace distributions??

Usually.

It really helps to "provide commented, minimal, self-contained, 
reproducible code" as you are asked, but you have not shown what dal() is.

If dal() is **vectorized**, then a vectorized approach like this will 
often work well:

rout <- matrix(0,n)
rejected <- seq(n)
k <- n
while( k > 0 ){
   rout[ rejected ] <- rnorm( k )
   ratio <- dal(val=rout[ rejected ], p=p)/(s*dnorm(rout[ rejected ]))
   alpha <- runif( k , min=0, max=1 )
   rejected <- rejected[ alpha >= ratio ]
   k <- length( rejected )
}

Obviously, I cannot test this without dal().

HTH,

Chuck

p.s. Putting a limit on iterations is usually a good idea, too.

Thanks in advance for your help and have a great weekend.

Regards

Anup

***********************************
ral <- function(n,p,s=3)
{
rout <- matrix(0,n)
for(i in 1:n)
{
 repeat
   {
     root <- rnorm(1)
     ratio <- dal(val=root, p=p)/(s*dnorm(root))
     alpha <- runif(1, min=0, max=1)
     if(alpha<ratio)
       {break}
     }
  rout[i,] <- root
}
return(rout)}



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Charles C. Berry                            (858) 534-2098
                                             Dept of Family/Preventive Medicine
E mailto:cberry at tajo.ucsd.edu	            UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/  La Jolla, San Diego 92093-0901