Hi! I''m running monte carlo simulations and I''m thus creating pseudo samples of an imaginary population. Of course vars in this population are correlated. There is no problem as far as they are purely numeric (normal or nonnormal). I use the chol() transformation of the correlation matrix to get the things done. Does anybody have any experience in including a dichotomous (binomial) variable to the show? I tried it in noumerous ways using all sorts of different starting distributions, however none seem to work... either I don''t get the wanted proportion in the final modification or I ruin the correlation with the final transformation. I would be very glad of any insight how to solve the problem... I give an example using binomial distribution as a start... I also tried skewed and nonskewed normal, and various beta distributions and none worked. n<-1000 #sample size p<-0.3 #proportion r12<-0.4 #correlation x1<-rbinom(n,1,p) x1<-(x1-p)/sqrt(p*(1-p)) x2<-rnorm(n,0,1) r<-array(1,c(2,2)) r[1,2]<-r12 r[2,1]<-r12 r.ch<-chol(r) d.1<-cbind(x1,x2) d.1<-d%*%r.ch d.1[,1]<-round((d.1[,1]*sqrt(p*(1-p)))+p) #d.1[,1]<-round(d.1[,1]+p)*sqrt(p*(1-p)) cor(d.1) mean(d.1[,1]) Andrej _________ Andrej Kveder, M.A. researcher Institute of Medical Sciences SRS SASA; Novi trg 2, SI-1000 Ljubljana, Slovenia phone: +386 1 47 06 440 fax: +386 1 42 61 493 [[alternate HTML version deleted]]