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
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