similar to: Bivariate Weibull distribution -- Copula

Dear R users I would like to calculate the Cumulative incidence for an event adjusting for competing risks and adjusting for covariates. One way to do this in R is to use the cmprsk package, function crr. This uses the Fine & Gray regression model. However, a simpler and more classical approach would be to implement the Kalbfleisch & Prentice method (1980, p 169), where one fits cause

Hi all, I am trying to build a copula model using the Gumbel Copula and I have two marginal distributions.I know the marginal parameters by using the fitdistr() and optim().The problem is I dont know my copula parameter. I am getting a bit confused of how shall I go about it.I read the previous threads where the query was to estimate all the parameters.How shall I define my log-likelihood

A few days ago I uploaded to CRAN a new package called 'eha', which stands for 'Event History Analysis'. Its main focus is on proportional hazards modeling in survival analysis, and in that respect eha can be regarded as a complement and an extension to the 'survival' package. In fact eha requires survival. Eha contains three functions for proportional hazards

A few days ago I uploaded to CRAN a new package called 'eha', which stands for 'Event History Analysis'. Its main focus is on proportional hazards modeling in survival analysis, and in that respect eha can be regarded as a complement and an extension to the 'survival' package. In fact eha requires survival. Eha contains three functions for proportional hazards

Justin, You have an interesting problem, and a serious (reliable) consultation would take more time than I have to give at the moment. Which is to say that you should take these comments with a grain of salt. First, I don't think that you have censored data. You have 2 subdistribution functions F1(t) and F2(t), F1(t) + F2(t) = F(t) = the "time to endpoint" distribution.

Hi, I'm dealing with wind data and I'd like to model their distribution in order to simulate data to fill-in missing values. Wind direction are typically following a vonmises distribution and wind speeds follow a weibull distribution. I'd like to build a joint distribution of directions and speeds as a VonMises-Weibull bivariate distribution. First is this a stupid question? I'm

Hello! I have a matrix with data and a column indicating whether it is censored or not. Is there a way to apply weibull and exponential maximum likelihood estimation directly on the censored data, like in the paper: Backtesting Value-at-Risk: A Duration-Based Approach, P Chrisoffersen and D Pelletier (October 2003) page 8? The problem is that if I type out the code as below the likelihood

Case cohort function cch() is in survival package. In cch(), the prentice method is implemented like this: Prentice <- function(tenter, texit, cc, id, X, ntot,robust){ eps <- 0.00000001 cens <- as.numeric(cc>0) # Censorship indicators subcoh <- as.numeric(cc<2) # Subcohort indicators ## Calculate Prentice estimate ent2 <- tenter ent2[cc==2] <-

Hi, I would like to implement the empirical copula in R, does anyone know if it is included in a package? I know it is not in the "Copula" package. This one only includes a gof-test based on the empirical copula process. Thanks for your help! Gregor -- View this message in context: http://www.nabble.com/Empirical-copula-in-R-tf4018319.html#a11412335 Sent from the R help mailing list

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

Hello I am writing to you regarding your interest in Copula/copulae and it?s usage. I am currently studying Copula for my Master thesis and also therefore have a large interest in it. Now I am looking for information regarding copula as well as Copula and "R". Code, information how to calculate or anything would be appreciated! Since there seem to be little information regarding

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")

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

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

>>>>> 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;

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 everybody, I try to simulate random numbers from a trivariate nested Archimedean copula. My aim is to correlate two processes with, e.g. theta2, as the so called child pair and then to correlate these two processes with a third one with theta1 (parent). This "figure" tries to capture what I am explaining theta1 theta2

Hey all, I'm trying to construct a 7-dimensional normal copula using the copula package. I'd like to supply as parameter a randomly generated correlation matrix (that I'll convert to a vector so I can feed it to the normalCopula function). What order do the pairwise correlations inside that vector have to be in? In other words: What does the normalCopula function "expect"?

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 =