similar to: Questons about R capabilities

Hi, I am using 'igraph' to make some plots. The problem I got is that I don't know how to label the nodes with gene names. My sample code: ## suppose I have 100 gene (nodes) ## --------------------------------------------------------------------------- graph <- set.vertex.attribute(graph, "color", value=c(rep(c('green','red'),50))) graph <-

Hello, today I download poolalloc from " https://github.com/llvm-mirror/poolalloc". and I compiled it with LLVM3.3. Then when I excute "make",I get the error: ​I correct the path of callsite.h. there is another error: ​ so I wonder if I get the wrong version, and where should I get the right version compiler with LLVM3.3? -------------- next part -------------- An HTML

Dear all, I would like to share a paper I co-authored with Prof. Alper Sen. This paper describes an algorithm to generate testbenches from SystemC models represented with LLVM IR. It was accepted and presented at International Conference on Hardware/Software Codesign and System Synthesis (CODES/ISSS), 2012. A link to the paper is accessible from acm or Alper's website:

Hello - I am a fairly new user of R. I currently have R1.7 installed. I tried to install the KernSmooth package but a message tells me I need R1.8. Is there a way to update to R1.8 while in R1.7 in the same manner that I can use "update.packages", or do I have to install R1.8 in a separate directory and re-install the many packages I have already installed in R1.7? Thanks in advance for

Hi, I have a big flac file, i can divide this flac file into chunks by visual basic code, i can add header to these chunks by copying first 1K to other chunks, but chunks can not play properly, i think i need to change filesize from file headers of these chunks, i can read samplerate and samples count from header, but i do not know how can i calculate file size without reading all file, what

I'm looking for design and hmisc version 2.0 for R 1.8 for windows. I've found design 2.0 in the downloads for R1.7 but not hmisc. I've also checked Dr. Harrell's site and it only goes to 1.6 for windows. Any thoughts? Shawn Way

I am trying to simulate draws from a multivariate normal using mvrnorm, and am getting the following error message: Error in mu + eS$vectors %*% diag(sqrt(pmax(ev, 0)), p) %*% t(X) : non-conformable arrays I do not understand why I am getting this message, since the vector of means I am giving to the function is 13 by 1 and the variance matrix I am giving to the function is 13

Dear list, I am trying to use the 'mvrnorm' function from the MASS package for simulating multivariate Gaussian data with given covariance matrix. The diagonal elements of my covariance matrix should be the same, i.e., all variables have the same marginal variance. Also all correlations between all pair of variables should be identical, but could be any value in [-1,1]. The problem I am

Hi all, Recently our statistics students noticed that their Gibbs samplers were crashing due to some NaNs in some parameters. The NaNs came from mvrnorm (Ripley & Venables' MASS package multivariate normal sampling function) and with some more investigation it turned out that they were generated by function eigen, the eigenvalue computing function. The problem did not seem to happen

Hello, does anybody know another faster function for random multivariate normal variable simulation? I'm using mvrnorm, but as profiling shows, my algorithm spends approximately 50 % in executing mvrnorm function. Maybe some of you knows much faster function for multivariate normal simulation? I would be very gratefull for advices. -- View this message in context:

Hi, I m trying to down load the binary for Win of R1.7 (called R 0.8... ?) from few mirrors but all are down... Is it normal ? Regards Philippe [[alternate HTML version deleted]]

Hi All This might be something very trivial but I seem to miss something in the syntax or logic which makes me keep wandering around the problem without arriving at a solution. What I want to do is to simulate a sample data for performing cluster analysis. I tried to use x1= mvrnorm(10,rep(0.8,3),diag(3)) x2= mvrnorm(10,rep(0,3),diag(3)) x3= mvrnorm(10,rep(-0.5,3),diag(3)) x=rbind(x1,x2,x3)

Microsoft Windows R users, I am operating Windows 2000 (build2195) with R1.7. It may be pertinent that I am using a dual head screen with the initial RGui filing the entire area of both screens. When starting R I use the .First() function to add menu items to the RGui interface using winMenuAdd() and winMenuAddItem(). The menus do not display until the RGui window is physically manipulated in

Hello. Somebody knows how can I generate a set of n random vectors of a normal distribution of several variables? For example, I want to generate n=100 random vectors of two dimensions for a normal with mean c(0,1) and variance matrix: matrix(c(2,1,1,3),2,2). Thanks in advance, Arnau.

Hi all. I was having some trouble with a for loop and I found the problem is the following. Does anyone have some idea why I got the following R result? Since mone is equal to 3, why mu1 only have 2 components? library(MASS) > p0 <- seq(0.1, 0.9,by=0.1) > m <- 10 > > > p0 <- p0[7] > > ## data generation > > mzero <- p0*m > mone <- m-mzero >

Dear all, I'm happy to share a recent publication on a bounded model checker for concurrent programs represented in LLVM IR that integrates the verification framework that I'm developing: Marcelo Sousa, and Alper Sen, LLVMVF: A Generic Approach for Verification of Multicore Software. Journal of Electronic Testing: Theory and Applications, September 2013. (

Hello, I have being conducting some (conditional logistic) regressions (clr), and I'm interested in extracting and manipulating (as a number) the p-values. (This is a prelude to some power simulations, where I'll generate data sets, perform a clr on each data set, and then extract the p-value for calculation of the power.) From what I can tell, the p-value is not one of the quantities

Dear List: I am generating N datasets using the following Sigma<-matrix(c(400,80,80,80,80,400,80,80,80,80,400,80,80,80,80,400),4,4 ) mu<-c(100,150,200,250) N=100 for(i in 1:N) { assign(paste("Data.", i, sep=''), as.data.frame(cbind(seq(1:1000),(mvrnorm(n=1000, mu, Sigma))))) } With these datasets, I need to work on some of the variables and then run each dataset

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