search for: wishart

Hello Everyone Both the MCMCpack and the bayesm libraries allow us to make draws from the Inverse Wishart distribution. But I wanted to find out how exactly is the Inverse Wishart distribution parameterized in these libraries. The reason I ask is the following: Now its generally standard to express Inverse Wishart as IW(0.5 * DOF,0.5* Scale). (DOF-> Degree of freedom, Scale -> Scale parame...

Hello everyone I was wondering how would one simulate from a Normal Wishart Distribution in R. A normal inverted Wishart distribution is denoted by NIW (M,C,d,S), where X/(Sigma) ~ N( M,C,(Sigma) ) -> a matrix normal distribution, (Sigma) -> column dispersion matrix (Sigma) ~ IW (d,S) -> inverted Wishart distribution Thanks a lot ! Best Shantanu [[alternat...

Dear R users, I am doing MCMC using Metropolis-Hastings. My model is bivariate-log-normal and the prior for variance-covariance is wishart distribution. I am wondering if there are some simple codes about how to get the density of Wishart distribution in my case ? Thanks in advance. Meihua [[alternative HTML version deleted]]

Hi, I am not sure if this make sense at all. I'd like to sample a matrix, which follows a wishart / inverted wishart distribution. However, the (1,1) element of this matrix should always be equal to 1. How can I handle it in R? Any suggestion is greatly appreciated. Thanks a lot. Sonia [[alternative HTML version deleted]]

If so, I''ve had trouble finding it. Can anyone help?

...h the "riwish" command in the package "MCMCpack". I've searched the documentation and can't seem to figure it out. For example: Define a matrix: > lam <- matrix(c(.00233,-.00057,-.00057,.00190),2,2) and then use the riwish command to generate a random inverse-Wishart variate: > riwish(72,lam) In version 2.1.0, the result is a matrix of very small numbers like: [,1] [,2] [1,] 3.323499e-05 -3.417600e-06 [2,] -3.417600e-06 2.283511e-05 In version 2.0.0, the result is a matrix of larger numbers like: [,1] [,2] [1,] 9.70...

...would need a C introduction only for MCMC and I do not know if such a thing exists. I was thinking of Python (and scipy). Where could I read about its integration into R ? How developed are the statistical packages in Python ? I could not find a Python package on the web with functions to simulate Wishart, or multivariate gamma or student distributions. Since I am a little bit lost, I write this message to the R help list. Sorry for these naive questions and thanks for your help. Best, Jean [[alternative HTML version deleted]]

Hi Thanks for replying my question! What really interested me is that the package providing some complex form sampling, such as wishart, multinomial, dirichlet. And others for example conditional beta distribution confining the random variable in the interval (a, b). Since these concept are widely used in the baysian, I wonder whether somebody has already written this package. Thanks! Best wishes, Peng ***************************...

Dear List, Is there any function in R to generate multivariate F distribution with given correlation/covariance matrix? Actually, I just want to generate some 2-dimentional non-normal data sets (skewed) for low (may be around 0.3 cor coeff.) negatively and also positively correlated variables ? Thanks in advance. Anna

I want to generate a valid variance-covariance matrix. One way could be to generate some random sample from multivariate normal distribution and then calculate cov. matrix. Another way could be to sample from wishart distribution itself. However both cases need a valid i.e. PD covariance matrix. As I need to generate that covariance matrix only, I am not interested those two methods. Can anyone suggest me some other way out? Regards,

....xy <- sd.c[1] * sd.c[2] * r * (n-1) A <- matrix(c(s.xx, s.xy, s.xy, s.yy), ncol=2) #estimation function if(standardized) { estimation <- function(patient, mean.c, n, A) { #estimation of a variance/covariance matrix (sigma) sigma = riwish(n,A) #random obs. from an inverse-Wishart distribution #estimation of the means (mu) z <- rnorm(2) T <- t(chol(sigma)) #Cholesky decomposition mu <- mean.c + T %*% z/sqrt(n) #standardization z.x <- (patient[1]-mu[1]) / sqrt(sigma[1,1]) z.y <- (patient[2]-mu[2]) / sqrt(sigma[2,2]) rho.xy <- sigm...

Respected R Users, I looking for help with generating theoretical confidence regions for any of non-symmetric bivariate statistical distributions (bivariate Chi-squared distribution<Wishart distribution>, bivariate F-distribution, or any of the others). I want to to used it as a benchmark to compare a few strategies constructing confidence regions for non-symmetric bivariate data. There is a paper proposed an accumulative function for bivaraite F-Distribution, but it was basically...

Dear All, Is there any function in R for computing "multivariate chi-square distribution"? How about "multivariate gamma distribution"? I appreciate any comment on this subject. Thank you, Amin Zollanvari PhD student Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX

Hola! Para simular matrices de datos normales multivariados con la sentencia rmvnorm (dentro del paquete mvtnorm) se necesita, entre otras cosas, el número de vectores a simular, el vector de parámetros-medias correspondiente a cada variable y su respectiva matriz de Varianza-Covarianza. En este último punto, tengo problemas. En lugar de ingresar una matriz sigma creada por mi, necesito simular

Hello, I am a chemical student and I make use of 'lme/lmer function' to handle experiments in split-plot structures. I know about the mcmcsamp and I think that it's very promissory. I would like knowing "the concept behind" of the mcmcsamp function. I do not want the C code of the MCMCSAMP function. I would like to get the "pseudo-algorithm" to understanding that

Hello R group Do you know if an EM algorithm exits for R to estimate missing data in a sample? I just found knn algorithm in to the package emv but it doesn't look to be the usual EM algorithm. Thanks Xavier -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Hi, Does anybody know, what is going on here? > diag(sqrt(1)) [,1] [1,] 1 > diag(sqrt(0.3333)) <0 x 0 matrix> > sqrt(1) [1] 1 > sqrt(0.3333) [1] 0.5773214 BR, Markku Karhunen researcher University of Helsinki

Greetings, I have a seemingly simple task which I have not been able to solve today and I checked all of the help archives on this and have been unable to find anything useful. I want to construct a symmetric matrix of arbtriray size w/o using loops. The following I thought would do it: p <- 6 Rmat <- diag(p) dat.cor <- rnorm(p*(p-1)/2) Rmat[outer(1:p, 1:p, "<")] <-

I am desperately trying to impute missing data using 'imp.mix' but always run into this yucky error message to which I cannot find the solution. It's the first time I am using mix and I'm trying really hard to understand, but there's just this one step I don't get...perhaps someone knows the answer? Thanks! Jens My code runs:

Dear friends, I work with Matlab and now a bit in trouble with getting used to R. Could you give me some help with the following questions: 1. how to generate the random matrix mxn with constant mean and variance, say N(0,1)? 2. how to create a code (function), say “myfunction”, and make it available for use every time I run R? 3. how to make a package, say “e1071”, available for use