[R] Trying to speed up an if/else statement in simulations

[David Winsemius]

On Jun 18, 2012, at 1:29 PM, nqf wrote:

> Dear R-help,
>
> I am trying to write a function to simulate datasets of size n which  
> contain
> two time-to-event outcome variables with associated 'Event'/'Censored'
> indicator variables (flag1 and flag2 respectively). One of these  
> indicator
> variables needs to be dependent on the other, so I am creating the  
> first and
> trying to use this to create the second using an if/else statement.
>
> My data structure needs to follow this algorithm (for each row of  
> the data):
> If flag1=1 then flag2 should be 1 with probability 0.95 and zero  
> otherwise
> Else if flag1=0 then flag2 should be 1 with probability 0.5 and zero
> otherwise
>
> I can set up this example quite simply using if else statements, but  
> this is
> incredibly inefficient when running thousands of datasets:
> data<-as.data.frame(rbinom(10,1,0.5))
> colnames(data)<-'flag1'
> for (i in 1:n) {
>  if (data$flag1[i]==1) {data$flag2[i]<-rbinom(1,1,0.95)} else
> {data$flag2[i]<-rbinom(1,1,0.5)}
> }
>
>
> I think to speed up the simulations I would be better changing to
> vectorisation and using something like:
> ifelse(data$flag1==1,rbinom(1,1,0.95),rbinom(1,1,0.5))
> but the rbinom statements here generate one value and repeat this  
> draw for
> every element of flag2 that matches the 'if' statement on flag1.
>
> Is there a way to assign flag2 to a new bernoulli draw for each  
> subject in
> the data frame with flag1=1?

If the parameters for the the Bernoulli draws stay the same, as they  
appear to do then all you need to do is read the help page for  
`rbinom` and use the appropriate call to create vectors that are as  
long as data$flag.


-- 
David Winsemius, MD
West Hartford, CT