Your explanation below has me more confused than before. Now it is possible that it is just me, but it seems that if others understood it then someone else would have given a better answer by now. Are you restricting your categorical and binary variables to be binned versions of underlying normals? if that is the case I doubt that there would be a more efficient way than binning a normal variable.
If not then can you show us more of what you want to produce? along with what you mean by correlation or covariance with categorical variables (which is meaningless without additional restrictions/assumptions). On Fri, Mar 30, 2012 at 3:41 PM, Burak Aydin <burak235...@hotmail.com> wrote: > Hello Greg, > Thanks for your time, > Lets say I know Pearson covariance matrix. > When I use rmvnorm to simulate 9 variables and then dichotomize/categorize > them, I cant retrieve the population covariance matrix. > > -- > View this message in context: > http://r.789695.n4.nabble.com/simulate-correlated-binary-categorical-and-continuous-variable-tp4516433p4520464.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > 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. -- Gregory (Greg) L. Snow Ph.D. 538...@gmail.com ______________________________________________ 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.
