similar to: multivariate sampling question again

Hi, Are there some packages which can generate multi-normal, multi-t, etc multivariate sampling? thanks! Best wishes, Peng ******************************* Peng Zhang Department of Biostatistics Harvard School of Public Health 655 Huntington Avenue Boston, Massachusetts 02115 ******************************* I believe I can fly I believe I can touch the sky

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

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

Hello All, Has anyone had success building 64-bit R on Solaris 10 Sparc with the Oracle Studio Compiler suite? Could anyone start give me a clue. I have tried to no avail. Thanks, Nynese Nynese Tinsley, BSEE, MSCIS UNIX Systems Analyst Harvard School of Public Health Center for Biostatistics in AIDS Research 651 Huntington Ave, FXB 614 Boston, MA 02115 617-432-3244 office#

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

Hello there, I just changed to FreeBSD platform, and want to install R on it. I use FreeBSD 5.0 and install nearly all packages on the machine. When I use ports to install R, (cd /usr/ports/math/R-letter, and then type make) I got the following error information. ../../../../library/methods/libs/methods.so is unchanged dumping R code in package 'methods' Fatal error: The X11 shared

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 [[alternative HTML version

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]]

Good Afternoon R Help! On of my users is seriously thinking about moving to R from Splus. In his testing, he has already found some benefits to it. He has, however run into one issue, calling his Fortran functions in R. Previously, in Splus, he would do the following: 1. Compile via f90 the *.f program 2. link the object file to the Fortran IMSL libraries via the 'ld' command 3.

Dear R users and developers After upgrading to Windows XP and R 1.9.1 and 2.0, I retried to execute plot.baysian() to a data set that I had used previously to plot with no problem in win2000 R1.8. The error I get is: Error in points(Mbar[-index], lods[-index], pch = ".") : only 0's may mix with negative subscripts Thanx in advance Dino P.S. I allready sent this message

Hi, I'm trying to follow the R-admin instructions for using MKL10 as the external BLAS compiling R-2.6.2 under Linux on a RH EL head node of a cluster. The configure process seems to have problems when it checks for dgemm in the BLAS. I'm using configure as: ./configure CC=icc F77=ifort --with-lapack="$MKL" --with-blas="$MKL" where $MKL is defined as in R-admin

R users- In moving from R 2.0.0 to R 2.1.0 in Windows, I have encountered a problem with 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

Hi, I have a small doubt regarding naive Bayes. I am able to classify the data's properly but i am just stuck up with how to get the probability values for naive bayes. In the case of SVM we have "attr" function that helps in displaying the probability values. Is there any function similar to "attr" in naive Bayes that can be used for displaying the attribute values. my

Dear R users, I would like to make my R code for MCMC faster. It is possible to integrate C code into R but I think C is too complicated for me. I 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

Hi, How can I generate data from multivariate gamma distribution & multivariate beta distribution? I only found command for multivariate normal only. Many thanks in advance :) Regards Rofizah [[alternative HTML version deleted]]

Dear r-help, I create a graph of my baysian network. I use the package igraph. The names of vertex are within the circle, I would leave them outside the circle? > E(g)$color <- "black" > tkplot(g, ,vertex.label=names,layout=layout.kamada.kawai, edge.color=E(g)$color) Best Regards [[alternative HTML version deleted]]

Hello, Is there any literature there that says that the EM is better/worse than a Baysian model when it comes to differentiating univariate mixture of normal distributions? -- Thanks, Jim. [[alternative HTML version deleted]]

I read through the editorial. This is the one of the most mega-ultra-super-biased articles I've ever read. e.g. The authors encourage Baysian methods, and literally encourage subjective approaches. However, there's only one reference to robust methods and one reference to nonparametric methods, both of which are labelled as purely exploratory methods, which I regard as extremely

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: