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...nal 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 vonmises-weibulldistribution look at the following code: library(copula) par(mfrow = c(2, 2)) x <- mvdc(normalCopula(sin(0.5 * pi /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(frankCopula(5.736276), c("norm", "norm"), list(list(mean = 0, sd = 1), list(mean...

Hi r-users, I try to fit the t copula using the gamma marginals.  But I got error message which I don't really understand. Thank you for any help given. myCop.t <- ellipCopula(family = "t", dim = 2, dispstr = "toep", param = 0.5, df = 8) myCop.t myMvd <- mvdc(copula = myCop.t, margins = c("gamma", "gamma"), paramMargins = list(list(mean = 0, sd = 2), list(mean = 0, sd = 1))) myMvd dat <- stn_pos ## observed data loglikMvdc(c(0, 2, 0, 1, 0.5,8), dat, myMvd)   ## loglikelihood mm <- apply(dat, 2, mean) vv <- apply(dat, 2, var)...

I am trying to make a loglikelihood function using copulas. I am trying to use mvdc to find the density function. When I run this I got the error that the pdf and cdf of my function tobit doesn't exist. Can somebody guide me where my mistake is? dtobit <- function(beta,sigma, x, y) {ifelse(y>0, dnorm(y,x%*%beta, sigma),(1-pnorm((x%*%beta)/sigma)))} ptobit <- function...

...felse(Y2>0, pnorm((Y2-x%*%beta)/sigma),(1-pnorm((x%*%beta)/sigma)))} dtobit3 <- function(beta,sigma,x,y3) {ifelse(Y3>0, dnorm(Y3,x%*%beta, sigma),(1-pnorm((x%*%beta)/sigma)))} ptobit3 <- function(beta,sigma,x,y3) {ifelse(Y3>0, pnorm((Y3-x%*%beta)/sigma),(1-pnorm((x%*%beta)/sigma)))} #mvdc object myMvdc <- mvdc(copula = ellipCopula("normal", param = c(rho12, rho13, rho23),dim = 3, dispstr = "un"), margins = c("tobit1", "tobit2", "tobit3"), paramMargins = list(list(beta=fit1$coef,sigma=sigma1), list(beta=fit2$coef,sigma=sigma2), lis...

...t;-function(per) {dexp(per,rate=0.5)} pper<-function(per){pexp(per,rate=0.5)} qper<-function(per){qexp(c(seq(0,1,.01)),per, rate=0.5)} gmb<-gumbelCopula(3,dim=2) # create bivariate copula object with dim=2 #tau(gmb) ## construct a bivariate distribution with defined marginals myCDF<- mvdc(gmb, margins=c("exp","exp"), 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 (bi...

Dear r list, I posted this on the S list last week since i'm using some of the FinMetrics functions on copula. Knowing there is a copula package in R, I figure this would be an appropriate forum to ask this question. I want to model inverse relationship between two (non-normal, non-symmetric) marginals with the gumbel copula, or with any copula. Say, x is lognormal and y is norm. Since

...cópula donde las funciones marginales son multivariantes, siguiendo el esquema del package ''Copula''. Es decir, Copula(F(x,y), G(w,z))) En el caso de funciones marginales univariantes, un ejemplo de la normal multivariante quedaria de la siguiente forma, copulagaussiana<-mvdc(normalCopula(vector_de_correlaciones,dim=3,dispstr ="un"),c(rep("norm",3)),list(param1,param2,param3)) donde ''vector_de_correlaciones'' es un vector con las componentes del triángulo superior de la matriz de correlaciones ordenadas adecuadamente. La función ent...

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

Sir, I want to draw random from any multivariate disrtibution. Is there any function in R to do this? Regards, Suman Dhara [[alternative HTML version deleted]]

Hi, I'd like to know why using the program "R" I can't add a number of margins> 3, I have a problem with the graphics. Post here my script: > myCop.norm <- ellipCopula(family = "normal", dim = 3, param = 0.4) > myMvd <- mvdc(copula = myCop.norm, margins = c("norm", "norm","norm"), > paramMargins = list(list(mean = 0, sd = 2), list(mean = 0, sd = > 1),list(mean=0,sd=2))) persp(myMvd, dmvdc, xlim = c(-4, 4), ylim=c(-4, 4),main="Normal copula") I obtain the following messag...

I want to use copula package in R to generate random vector of multivariate F distribution with a pre-specified diagonal covariance matrix, say, diag(2, 3, 0, 0, 0). Can someone tell me how I can specify the diagonal covariance matrix in the copula function "mvdc"? Thank you very much.

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:

...rking.   loglik.marg <- function(b, x) sum(dgamma(x, shape = b[1], scale = b[2], log = TRUE))   ctrl <- list(fnscale = -1)   #dat <- stn_pos[,1:2] ## observed data myCop.t <- 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&quo...

...loglik.marg <- function(b, x) sum(dgamma(x, shape = b[1], scale = b[2], log = TRUE)) ctrl <- list(fnscale = -1) #dat <- stn_pos[,1:2] ## observed data myCop.t <- 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="spea...

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