On Fri, 2008-03-21 at 22:37 -0700, Sherri Rose wrote: > Dear R-Help List, > I'm trying to simulate data from a conditional distribution, and > haven't been able to modify my existing code to do so. I searched > the archives, but didn't find any previous post that matched my > question. > > n=10000 > pop = data.frame(W1 = rbinom(n, 1, .2), > W2 = runif(n, min = 3, max = 8), W3 = rnorm(n, mean=0, sd=2)) > pop = transform(pop, > A = rbinom(n, 1, .5)) > pop = transform(pop, > Y = rbinom(n, 1, 1/(1+exp(-(1.5*A-.05*W1-2*W2-2*W3+2*A*W1))))) > > In this population the probability of being "diseased" (Y=1) is > approx 0.030. What I want to be able to do is specify a conditional > distribution of (A, W1, W2, W3) given that Y=1 and one for (A, W1, > W2, W3) given that Y=0. Then I can sample diseased and non-diseased > individuals from these distributions without having to simulate a > large base population. This will be particularly useful when the > probability of being "diseased" is even smaller and I want a large > number of diseased individuals. > > Any pointers to do this would be extremely helpful! > Thank you, > Sherri Rose > > UC Berkeley > [[alternative HTML version deleted]]
If I understand your problem, this script solve your question: n<-10000 Y<-rbinom(n,1,.3) A<-ifelse(Y==0,1,rbinom(n, 1, .5)) W1<-ifelse(Y==0,1,rbinom(n, 1, .2)) W2<-ifelse(Y==0,1,runif(n, min = 3, max = 8)) W3<-ifelse(Y==0,1,rnorm(n, mean=0, sd=2)) pop<-data.frame(Y,A,W1,W2,W3) pop -- Bernardo Rangel Tura, M.D,MPH,Ph.D National Institute of Cardiology Brazil ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
