Hello, you can use something like that
eqf <- function(x, sampleMarg) as.numeric(quantile(sampleMarg, probs=x)) and apply eqf on each marginal after the copula fit if you want to generate random samples or directly ecdf if you want to compute multivariate distribution function. Christophe -- Christophe Dutang Ph.D. student at ISFA, Lyon, France website: http://dutangc.free.fr Le 19 janv. 2011 à 21:50, salmajj a écrit : > > Hi all, > I understand that rmvdc generates random number from mvdc object. But the > mvdc object can only be used if we define the marginals! So my question is > suppose we don't find any distribution which fit marginals so we use the > Canonical Maximum Likelihood method (This approach uses the empirical CDF of > each marginal distribution to transform the observations into pseudo > observations with uniform margins) SO after finding the copula which fit the > dependancy HOW i can generate random number which mimic the data? > Hope my question is clear, please if someone have an idea help me! > THANKS > > -- > View this message in context: > http://r.789695.n4.nabble.com/Copula-and-Multivariate-distribution-tp3225448p3225448.html > Sent from the Rmetrics mailing list archive at Nabble.com. > > _______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-finance > -- Subscriber-posting only. If you want to post, subscribe first. > -- Also note that this is not the r-help list where general R questions > should go. _______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
