similar to: How to specify covariance matrix in copula?

Hola, Quiero saber si es posible programar una 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,

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

Hello, I am trying to do a fit a loglikelihood function with Multivariate distribution via copulas with fitMdvc. The problem is that it doesn't recognize that my beta is a vector of km parameter and when I try to run it it say that the length of my initial values is not the same as the parameter. Can somebody guide me where my mistake is. Thanks, Elisa. #################################

Dear users I am reposting this and hope it will be accepted this time. I am using copula package to fit my bivariate data and simulation. As explained in package documentation we can use our own data distribution to feed on copula as long as we have d, p and q (pdf, cdf and quantile) functions are available. Hence my code for those are: # Make the functions for data distribution

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

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(beta,sigma, x,

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

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

Hi r-users,   I really need help in fitting the t-copula.  I try to reproduce the example given by Jun Yan in “Enjoy the joy of copula” but I’m not sure how to correct the error based on the error message.  I tried so many ways but still could not get it working.   loglik.marg <- function(b, x) sum(dgamma(x, shape = b[1], scale = b[2], log = TRUE))   ctrl <- list(fnscale = -1)   #dat <-

Hi r-users, I really need help in fitting the t-copula. I try to reproduce the example given by Jun Yan in "Enjoy the joy of copula" but I'm not sure how to correct the error based on the error message. I tried so many ways but still could not get it working. loglik.marg <- function(b, x) sum(dgamma(x, shape = b[1], scale = b[2], log = TRUE)) ctrl <- list(fnscale

I've only been using R on and off for 9 months and started using the glasso package for sparse covariance estimation. I know the concept is to shrink some of the elements of the covariance matrix to zero. However, say I have a dataset that I know has some underlying "baseline" covariance/correlation (say, a value of 0.3), how can I change or incorporate that into to the

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

Hello, I have a bigger problem in calculating the Maximum Likelihood Estimator regarding the degree of freedom of a multivariate skew-t copula. First of all I would like to describe what this is all about, so that you can understand my problem: I have 2 time series with more than 3000 entries each. I would like to calculate a multivariate skew-t Copula that fits this time series. Notice:

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

Hello folks! I'm using R-Package {vars} and I'm trying to estimate an A-Model. I have serious problems regarding the restrictions. 1) My A-Matrix needs (!) to have the following form: # 1 NA NA NA # 0 1 NA NA # 0 0 1 NA # 0 0 0 1 That is done in R by: A_Matrix <- diag(4) # main diagonal = 4 restrictions A_Matrix [1, 2] <- NA # A_Matrix [1, 3] <- NA #

Hi, I am trying to fit t copula to some data, and I am using the following function in the library(QRMlib). Udatac <- apply(datac, 2, edf,adjust=1) tcopulac <- fit.tcopula.rank(Udatac) But the error message come out "Error in fit.tcopula.rank(Udatac) : Non p.s.d. covariance matrix" Could anyone give me some advice? In fact, I am not sure what the "adjust=1" is used for.

Hello R-Team, I am trying to construct a Copula from a multivariate Gamma distribution with its marginals gamma-distributed. The multivariate Gamma should be able to contain a correlation coeficient or matrix. I have studied the book "Continuous Multivariate Distributions vol.I Models and applications" by Johnson & Kotz & Balakrishnan but what I constructed doesn't really

Dear list, I am trying to use the 'mvrnorm' function from the MASS package for simulating multivariate Gaussian data with given covariance matrix. The diagonal elements of my covariance matrix should be the same, i.e., all variables have the same marginal variance. Also all correlations between all pair of variables should be identical, but could be any value in [-1,1]. The problem I am

>>>>> Soumen Banerjee <soumen08 at gmail.com> >>>>> on Sat, 21 Apr 2018 17:22:56 +0800 writes: > Hello! I am trying to fit a copula to some data in R and > I get the error mentioned above. This is the code for a > reproducible example - (not really reproducible: You did not set the random seed, so the data is different every time;

Dear all, I just came to R a few days ago. Now I have a problem that I have two correlated variables and want to first fit a Gaussian copula, then sample it to generate simulated variables. I have spent last two days looking at R archive and copula help file but couldn't find what I need. If my understanding is correct, all examples I saw work in this way: a man-made copula -> simulated