Hello Greg, Sorry for the confusion. Lets say, I have a population. I have 6 variables. They are correlated to each other. I can get you pearson correlation, tetrachoric or polychoric correlation coefficients. 2 of them continuous, 2 binary, 2 categorical. Lets assume following conditions; Co1 and Co2 are normally distributed continuous random variables. Co1-- N (0,1), Co2--N(100,15) Ca1 and Ca2 are categorical variables. Ca1 probabilities =c(.02,.18,.28,.22,.30), Ca2 probs =c(.06,.18,.76) Bi1 and Bi2 are binaries, Marginal probabilities Bi1 p= 0.4, Bi2 p=0.5. And , again, I have the correlations.
When I try to simulate this population I fail. If I keep the means and probabilities same I lost the correct correlations. When I keep correlations, I loose precision on means and frequencies/probabilities. See these links please http://www.mathworks.com/products/statistics/demos.html?file=/products/demos/shipping/stats/copulademo.html http://stats.stackexchange.com/questions/22856/how-to-generate-correlated-test-data-that-has-bernoulli-categorical-and-contin http://www.springerlink.com/content/011x633m554u843g/ -- View this message in context: http://r.789695.n4.nabble.com/simulate-correlated-binary-categorical-and-continuous-variable-tp4516433p4524863.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.
