search for: claytoncopula

...ew values of the parameter (from M-step) and plug it in the weight, to get a new value of the weight. Then, iterate till converges. I tried the following code, but it does not work. library(copula) library(VineCopula) ## to generate the data set.seed(123) cp <- mixCopula(list(frankCopula(4),claytonCopula(2))) cop <- rCopula(100,cp) x <- pobs(cop) ## this is the data ## my function including optim function myfunc <- function(data,copula=list(frankCopula(4,dim=2), claytonCopula(0.5,dim=2)),maxit=200){ ? ??# copula[[1]]@parameters <- par[1] ? # copula[[2]]@parameters <- par[2] ? optim_...

Hi all, I am going to sample two variables from two exponential distributions, but I want to specify a covariance structure between these two variables. Is there any way to do it in R? Or is there a "Multivariate Exponential" thing corresponding to the multivariate normal? Thanks in advance. Sincerely, Yanwei Zhang Department of Actuarial Research and Modeling Munich Re America Tel:

Dear I estimate a sample selection model using the Clayton copula and Burr and Gaussian marginal. I need to derive ther Kendall'sw tau from the concordance coefficient by integration. I came across a way to do that in R long time ago but cannot find it again. Can somewone tell me what to read and what to use? Thank you. Steven Yen

...w working on Archimedean copulas and try to test the goodness of fit. Which packages I should use? I have Clayton copula with parameter (5.35) and Frank (19.5). I found this build function wrote by Yan and Ivan via R Packages, but I'm not sure the matrix for x? Please advice. e.g gofCopula(claytonCopula(1),x) Thank you Regards, Fayyad [[alternative HTML version deleted]]

...an = 0, sd = 1), list(mean = 0, sd = 1))) contour(x, dmvdc, xlim = c(-2.7, 2.7), ylim = c(-2.7, 2.7)) x <- mvdc(gumbelCopula(2), c("norm", "norm"), list(list(mean = 0, sd = 1), list(mean = 0, sd = 1))) contour(x, dmvdc, xlim = c(-2.7, 2.7), ylim = c(-2.7, 2.7)) x <- mvdc(claytonCopula(2), c("norm", "norm"), list(list(mean = 0, sd = 1), list(mean = 0, sd = 1))) contour(x, dmvdc, xlim = c(-2.7, 2.7), ylim = c(-2.7, 2.7)) the values of the association parameter I've chosen in each copula correspond to Kendall's tau 0.5; assuming also standard normal...

Greetings, I'm interested in generating data from various bivariate or mulitivariate distributions (e.g. gamma, t, etc), where I can specify the parameter values, including the correlations among the variables. I haven't been able to dig anything up on the faq, but I probably missed something. A nudge in the right direction would be appreciated. David --