similar to: Simulated mixed distribution multivariate data

(apologies if this looks like a re-post, I just sent a similar message to the r-help mail list. This version is via Nabble.) My intended application is error propagation using the ISO GUM Supplement 1 approach (propagation of distributions using Monte Carlo strategies). To automate uncertainty analysis I typically have the following data: (1) a measurement function y(x1,x2,...xn) (2) 'n'

Hello, from a search of the archives and functions, I am looking for information on creating random correlated counts from a multivariate Poisson distribution.  I can not seem to find a function that does this. Perhaps, it has not yet  been created. Has anyone created an R package that does this.   thanks,   Jourdan Gold     [[alternative HTML version deleted]]

Dear All,   thanks to Berend, my question posted yesturday was solved succesfully here: http://r.789695.n4.nabble.com/hep-on-arithmetic-covariance-conversion-to-log-covariance-td4646068.html . I posted the question with the assumption of using the results with rlnorm.rplus() from compositions. Unfortunatelly, I am not getting reasonable enough outcome. Am I applying the results wrongfully? The

Hi all, How can a multivariate Poisson time series be modeled? Aspects of glm, forecast, dse and dynlm seem relevant but not quite complete--but hopefully what I am missing is how to assemble them effectively. What I am looking to do is model my dependent variable y_t as a Poisson family function of lags of several independent variables and lags of y_t. I would like to include all lags up

Hello all: I'm sure this is a trivial request, but I'm still a beginner at this, and haven't been able to find it. I need to create simulated data based on some empirical distributions of a single variable. I've found R functions to help me simulate data based on analytical distributions, or to make simulations based on correlation matrices, but nothing so simple as what I need.

Dear everyone, I am having problem simulating multivariate data. Though I was able to simulate the data, but finding the variance-covariance matrix of simulated data did not give exact covariance matrix used in simulating the data. Unlike some other packages, like stata, using command "corr2data" will simulate data having the covariance matrix exactly with the specified covariance

Hello, I would like to simulate randomly from a multivariate normal distribution using a correlation matrix, rho. I do not have sigma. I have searched the help archive and the R documentation as well as doing a standard google search. What I have seen is that one can either use rmvnorm in the package: mvtnorm or mvrnorm in the package: MASS. I believe I read somewhere that the latter was

Dear R Users, I'm working on a problem where I have a multivariate response vector of counts and a continuous predictor. I've thought about doing this the same way you would do a Multvariate regression model with normally distributed data, but since these data are counts, they are probably better modeled with a Poisson distribution. For example y1<-rpois(100,3.5) y2<-rpois(100,1.5)

Dear?All, ? a few weeks ago I have posted a question on the R help listserv that?some of you have responded to with a great solution, would like to thank you for that? again.?I thought I would reach out to you with the issue I am trying to solve now. I have posted the question a few days ago, but probably it was not?clear enough, so I thought i try it again.?At times I have a multivariate example

Hi Folks, I want to do multivariate regression in R, i.e. basically (but with a complication -- see below): given an Nxp matrix Y of p-variate responses, and an Nxk matrix X of covariates, to fit the model Y = X*B + e with estimation of the kxp matrix of coefficients B and estimation of the pxp matrix of covariances between the p variates in Y. I haven't managed to find a

Dear List: I found an error when I called the 'gee' function. I cannot solve and explain it. There are no errors when I used the 'geeglm' function. Both functions fit the gee model. The project supervisor recommends me to use the 'gee' function. But I cannot explain to him why this error happens. Would you help me solve this problem? I appreciate your help. In

Dear R Users, I employed the phyper() function to estimate the likelihood that the number of genes overlapping between 2 different lists of genes is due to chance. This appears to work appropriately. Now i want to try this with 3 lists of genes which phyper() does not appear to support. Some googling suggests i can utilize the Multivariate hypergeometric distribution to achieve this. eg.:

Sir, I am working on fitting distribution on multivariate financial data and then simulate observations from that fitted distribution. I use stepAIC.ghyp() function of 'ghyp' library which select the best fitted distribution from generalized hyperbolic distribution class on the given dataset. data(indices) # Multivariate case: aic.mv <- stepAIC.ghyp(indices, dist =

Dear all R users, I wanted to calculated a sample Variance covariance matrix of a five-variate normal distribution. However I stuck to calculate each element of that matrix. My question is should I calculate ordinary variance and covariances, taking pairwise variables? or I should take partial covariance between any two variables, keeping other fixed. In my decent opinion is I should go for the

Hi all, rmvnorm()can be used to generate the random numbers from a multivariate normal distribution with specified means and covariance matrix, but i want to specify the correlation matrix instead of covariance matrix for the multivariate normal distribution. Does anybody know how to generate the random numbers from a multivariate normal distribution with specified correlation matrix? What about

I have put the new package bindata to CRAN, which provides a method for creating binary (i.e., 0-1-valued) random variables with correlation structures by converting multivariate random variables to binary variables. The package includes a postscript file of a technical report describing the method, here's the abstract: ********************************************************** The

I have put the new package bindata to CRAN, which provides a method for creating binary (i.e., 0-1-valued) random variables with correlation structures by converting multivariate random variables to binary variables. The package includes a postscript file of a technical report describing the method, here's the abstract: ********************************************************** The

I must to create an array with dimensions 120x8x500. Better I have to make 500 simulations of 8 series of return from a multivariate normal distribution. there's the command "mvrnorm" but how I can do this repeating the simulation 500 times?" [[alternative HTML version deleted]]

Hi, Forgive me if I seem naive, I'm tackling multivariate stats for the first time! Q. I'd like to know if there are packages that can be used to simulate random draws from general multivariate (joint) PDF functions when ONLY the independent marginal PDFs are known (RV means and covariance or correlation matrix)? Q. I see there is a Markov Chain Monte Carlo package, but the mcmc

Hi R-Users, I have a student doing work with lionfish and she has been trying to analyse a multivariate dataset to see what variables/factors are influencing the behaviour of lionfish. We have attempted a number of analyses, including rpart, relimpo and standard linear regression but we are not having much luck with quality output. The data is very non-normal and we would appreciate some advice