Hello,
Try creating an index vector. In this case, I've called it 'one'.
Then, 'n.one' is the number of ones in flag1.
(Since data is an R function, I've renamed your example dat).
n <- 10 # number of observations
# First make up some data
set.seed(123)
dat <- data.frame(flag1=rbinom(n, 1, 0.5))
dat$flag2 <- integer(n) # creates flag2, all zeros.
one <- dat$flag1 == 1 # logical index vector
n.one <- sum(ix) # how many
dat$flag2[one] <- rbinom(n.one, 1, 0.95) # whenever TRUE, set
dat$flag2[!one] <- rbinom(n - n.one, 1, 0.50) # negation of 'one'
dat
Simple, no? And always faster.
Hope this helps,
Rui Barradas
Em 18-06-2012 18:29, nqf escreveu:
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?
I hope my question is clear, and thank you in advance for your help.
Thanks,
Natalie
PhD student, Reading University
P.S. I am using R 2.12.1 on Windows 7.
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