similar to: Asymmetric t-copula code?

Hi R-users, Where can I find the code for asymmetric t-copula in R? Thank you for any help given. [[alternative HTML version deleted]]

Hi R-users, Where can I find the code for asymmetric t-copula in R? Thank you for any help given. [[alternative HTML version deleted]]

Hi R-users, I would like to use the copula package.? I? the package plus the mvtnorm and try to run the example given, but I got the following message: install.packages(repos=NULL,pkgs="c:\\Tinn-R\\copula_0.8-3.zip") norm.cop <- normalCopula(c(0.5, 0.6, 0.7), dim = 3, dispstr = "un") t.cop <- tCopula(c(0.5, 0.3), dim = 3, dispstr = "toep", df = 2, df.fixed =

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

Dear All, I'm now 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

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

I'm a new user of R and a novice user in copula R package. I want to fit 3-dimensional t copula for my trivariate data. So I used the command t.cop <- tCopula(c(0.785,0.283,0.613),dim=3,dispstr="un",df=6,df.fixed = TRUE) where c(0.785,0.283,0.613) is the correlation pattern of my data with 0.785 pearson correlation between variable 1-2, 0.283 correlation between 1-3 and 0.613

Hi, I'm a new user of R. I'm using package copula implemented in R. I want to know how to test goodness of fit of student's t copula for 3 dimensional cases using real world data. In manual gof test has been performed on the copula families generated from rcopula function that i understood as random data generated from copula? How to perform the test with real/observed data matrix.

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 everybody, I have a big problem that I do not manage to solve ! I will be very grateful if you can solve this ! I want to fit a t Copula with the copula package : > student.cop <- ellipCopula("t", param = c(0.5, 0.6, 0.7), dim = 3, dispstr = "un",df=5) > x<-rcopula(student.cop,1000) > fit <- fitCopula(x, student.cop, c(0.5,0.5,0.5,5)) And there is an

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 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 <-

I need to fit a t copula with fixed degree of freedom let's say 4. I do not want to estimate the dof together with correlation matrix optimally. Instead fix the dof to 4 and only estimate the correlation matrix in the optimization routine. Is anyone aware of such estimation method in R. The packages and functions that I know of can't do this estimation. I searched online but

Is anybody using the Copula package in R? The particular problem I'm facing is that R is not acknowledging the fitCopula command/function when I load the package and (try to) run something very simple: fit1 <- fitCopula(x1 = list(u11,u12,u13,u14,u15,u16,u17,u18), tCopula, optim.control = list(NULL), method = "BFGS") Anybody also using it, successfully or unsuccessfully?

Hi all, I need to test gof of 3-dimensional t copula for my trivariate observed data set. So I used the command t.cop <- tCopula(c(0.785,0.283,0.613),dim=3,dispstr="un",df=6,df.fixed = TRUE) where c(0.785,0.283,0.613) is the correlation pattern of my data with 0.785 pearson correlation between variable 1-2, 0.283 correlation between 1-3 and 0.613 is the correlation between variable

Hi all, I have this data set: ## Empirical copula ## dt1 = ranking ## dt2 = observed uniform data associated with the ranking   Sample data, > dt1         S_i   R_i  [1,]   7.0  10.0  [2,] 232.5 440.5  [3,] 143.0 141.5  [4,] 272.5 222.0  [5,]  46.0  34.0  [6,] 527.0 483.0  [7,] 420.5 563.5  [8,]  23.5  16.5  [9,]  56.5  68.5 [10,] 341.5 382.5   > dt2       unisk1 unisk2  [1,]  0.008  0.010

Hi, Is anybody using Copula package for fitting copulas to own data? I have two marginals Log Normal with (parameters 1.17 and 0.76) and Gamma ( 2.7 and 1.05) Which package I should use to fit Gumbel and Clayton Copulas? Thanks, fayyad [[alternative HTML version deleted]]

>>>>> 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

Hi, I want to run a quantile regression (Y=a+bX+e) using normal and t copula for my dissertation. I 've read the documentation of "copula" and "copBasic". However, I still have difficulty to deal with my data. Details are as following: I've already loaded xls data into r using "XLConnect" package. excel.file<-file.path("Q:/dailyvstoxx.xls")