search for: rmvdc

...paramMargins=list(list(rate=0.5),list(rate=0.5))) # Use own data for bivariate CDF construction myCDF2<- mvdc(gmb, margins=c("SAR","per"), paramMargins=list(list(rate=.5),list(rate=.5))) # Generate (bivariate) random numbers from that, and visualize x <- rMvdc(1000, myCDF2) And I get error message everytime as: > x <- rMvdc(1000, myCDF2) Error in qSAR(x, rate = 0.5) : unused argument(s) (rate = 0.5) It works fine with myCDF and generate bivariate data: x <- rMvdc(1000, myCDF2) But my problem is simulated data (using myCDF) does not show the...

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:

...ellipCopula(family = "t", param = 0.5,dim = 2, dispstr = "un", df = 8) myCop.t   myMvd <- mvdc(copula = myCop.t, margins = c("gamma", "gamma"), paramMargins = list(list(shape = 1.5, scale = 38), list(shape = 1.7, scale = 50))) myMvd n <- 200 dat <- rmvdc(myMvd, n)   mm <- apply(dat, 2, mean) vv <- apply(dat, 2, var) rho <- rcorr(dat,type="spearman")[[1]]; round(rho,2) rbind(mm,vv)   b1.0 <- c(mm[1]^2/vv[1], vv[1]/mm[1]) b2.0 <- c(mm[2]^2/vv[2], vv[2]/mm[2]) a.0  <- sin(cor(dat[, 1], dat[, 2], method = "kendall"...

...opula(family = "t", param = 0.5,dim = 2, dispstr = "un", df = 8) myCop.t myMvd <- mvdc(copula = myCop.t, margins = c("gamma", "gamma"), paramMargins = list(list(shape = 1.5, scale = 38), list(shape = 1.7, scale = 50))) myMvd n <- 200 dat <- rmvdc(myMvd, n) mm <- apply(dat, 2, mean) vv <- apply(dat, 2, var) rho <- rcorr(dat,type="spearman")[[1]]; round(rho,2) rbind(mm,vv) b1.0 <- c(mm[1]^2/vv[1], vv[1]/mm[1]) b2.0 <- c(mm[2]^2/vv[2], vv[2]/mm[2]) a.0 <- sin(cor(dat[, 1], dat[, 2], method = "kend...

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 --

hi, first I want to say that I'm new here, and new with copula and R. That is the reason why I'm writing, if somebody can help me. I have to make an example of Copula. On internet I've found this forum and that copula can calculate with R. Can somebody help me with the thing how can I start and where can read about these stuffs. Thank to all who can help! -- View this message

Thanks Dimitris!!! That's much clearer now. Still have a lot of work to do this weekend to understand every bit but your code will prove very useful. Cheers, Aziz -----Original Message----- From: Dimitrios Rizopoulos [mailto:Dimitris.Rizopoulos at med.kuleuven.be] Sent: May 12, 2006 4:35 PM To: Chaouch, Aziz Subject: RE: [R] Maximum likelihood estimate of bivariate