similar to: how to get samples from rtmvnorm with large dimensions

Hello! I was wondering if it's possible to see the actual code of checkTmvArgs function that is part of the code for rtmvnorm (which is below - I just typed "rtmvnorm" on the prompt). I get an error: Error in checkTmvArgs(mean, sigma, lower, upper) : sigma must be a symmetric matrix At the same time I am pretty sure that the matrix I am passing as sigma is a var-covar matrix

Hello! I am trying to generate a sample from a truncated multivariate normal distribution using rtmvnorm. I am using Gibbs because my alpha (line below) is teeny-tiny ( 4.083475e-64 ) alpha = pmvnorm(lower=lower, upper=upper, mean=btilde, sigma=MyVarCovar). When I try my Gibbs run, it takes quite a long time (with 10,000 iterations and 5,000 iterations burn-in) ~ 22 sec. And then what I get is

When sampling from a multi-variate truncated normal using rtmvnorm from the tmvnorm-package, I experience extreme performance differences between two of my computers. On my laptop computer, draws take ~5s, on my desktop ~30s. I need to run MCMCs with repeat calls to rtmvnorm on my desktop. The code in both cases is exactly the same. rtmvnorm seems not to have any machine dependent defaults

I want to use the rtmvt from the {tmvtnorm} package using the "gibbs" algorithm but how to i specify the nested function rtmvnorm to use gibbs as well? Right now I am using the code: for (i in 1:g){ for (j in 1:n){ sgamma[,,i,j] = rtmvt(n=50, mean=mu[i,j], sigma[i,j], df=nu[i], lower=rep(0,2),algorithm="gibbs") } } heres an example of one iteration: >

Dear R users, the tmvtnorm package, the package for the truncated multivariate normal and Student-t distribution, has been updated on CRAN. The major changes in version 1.0-2 (2010-03-04) are: * The package now provides methods for the truncated multivariate Student-t distribution, i.e. random number generation, density function, distribution functions like rtmvt(), dtmvt() und ptmvt() and

Dear R users, the tmvtnorm package, the package for the truncated multivariate normal and Student-t distribution, has been updated on CRAN. The major changes in version 1.0-2 (2010-03-04) are: * The package now provides methods for the truncated multivariate Student-t distribution, i.e. random number generation, density function, distribution functions like rtmvt(), dtmvt() und ptmvt() and

I have a server thats been running fine for a year or two lock up a few times recently, requiring power cycling. The /var/log/messages after a lockup last night is appended to this message. hardware is a pretty typical server, Supermicro X8DTE-F motherboard, dual Xeon X5650, 48GB ECC memory, LSI SAS 2008 for the boot disks, and LSI MegaRAID SAS 9261-8i for the data volume. Lots of 3TB

a previously rock solid reliable server of mine crashed last night, the server was still running but eth0, a Intel 82574L using the e1000e driver, went down. The server has a Supermicro X8DTE-F (dual Xeon X5650, yada yada). server is a drbd master, so that was the first thing to notice network issues. Just a couple days ago I ran yum update to the latest, I do this about once a month.

Given a square matrix of variables X, is there any way to compute a multivariable correlation among all the variables in the array? It is possible to calculate the correlation of all pairs of variables in the array, but I want to know the correlation of all the variables taken together, i.e. for the matrix X=x1 x2 x3 x4 x5 x6 x7 x8 x9 I don't want the pair-wise correlations

Dear All I am trying to include sampling weights in multilavel regression analysis using packege lme4 using following codes print(fm1 <- lmer(DC~sex+age+smoker+alcohol+fruits(1|setting), dataset,REML = FALSE), corr = FALSE) print(fm2 <- lmer(DC~sex+age+smoker+alcohol+fruits(1|setting), dataset,REML = FALSE), corr = FALSE,weights=sweight) The problem is both the

I really hope someone has a clue on this one.... :) I've got a 2.2TB array mounted on a 3ware 9500 controller. I installed it last week ensuring that LBA was enabled and using gpt with parted to get the full size available on one array. I also think I chose reiserfs for the array. We then moved ALOT of data to the array. Problem now is that after a reboot LBA doesn't seem to be in

Hi Tincs Groups, I try to set up Tinc-1.0pre 3 on RedHat kernel 2.2.14. I think i setup it's ok. Because it's show message like this. ********************************************************************** Site A May 22 07:30:00 sg1 tinc[802]: tincd 1.0pre3 (Apr 5 2001 02:02:52) starting, debug level 5 May 22 07:30:00 sg1 tinc[802]: Ready: listening on port

Hi, I recently ran into the problem that I needed a Siegel-Tukey test for equal variability based on ranks. Maybe there is a package that has it implemented, but I could not find it. So I programmed an R function to do it. The Siegel-Tukey test requires to recode the ranks so that they express variability rather than ascending order. This is essentially what the code further below does. After the

Hi, I am a newbie of R, and playing with the "ifelse" statement. I have the following codes: ## first, for(i in 1:3) { for(j in 2:4) { cor.temp <- cor(iris.allnum[,i], iris.allnum[,j]) if(i==1 & j==2) corr.iris <- cor.temp else corr.iris <- c(corr.iris, cor.temp) } } this code is working fine. I also tried to perform the same thing in another way with "ifelse":

A friend bring and Ipod, initially it didn't start because of charge, so I connected to my PC to give some charge, after that my linux didn't mount it, and when I start the iPod it shows me a folder with a sign, and that's all, nothing else matters. when I connected the iPod to the PC it start to charge and suddenly stops and shows the folder I have talked before I run the command

Dear R users, Does the results below make any sense? Can the the interval of the correlation coefficient be between *-1.0185* and -0.8265 at 95% confidence level? Liviu > library(boot) > data(mtcars) > with(mtcars, cor.test(mpg, wt, met="spearman")) Spearman's rank correlation rho data: mpg and wt S = 10292, p-value = 1.488e-11 alternative hypothesis: true rho is not

Hello, I m trying to set vector width using #pragma clang loop vectorize_width(32) but i m getting width 8 for the following kernel; #define M 128 #define N 128 #define SQRT_FUN(x) sqrtf(x) int main(int argc, char** argv) { /* Variable declaration/allocation. */ double float_n = (double)N; double data[N*M]; double corr[M*M]; double mean[M]; double stddev[M]; uint32_t

Two friend reported me a problem, which I can't solve: (I run R-1.0.0, Debian Linux) They hava a function "corr.matrix" (see end of mail), and when they create a 173x173 matrix with this function V <- corr.matrix(0.3, n=173) V1 <- qr.solve(V) reports: Error in qr(a, tol = tol) : NA/NaN/Inf in foreign function call (arg 1) For n < 173, qr.solve returns the correct

I feel like a complete dolt, as I know this question has been asked by others on a fairly regular basis, but I'm going in circles trying to get the following to work: id.prob<-function (tt) { library(mvtnorm) #============================ Makeham<-function(tt) { a2=0.030386513 a3=0.006688287 b3=0.039047537 t<-tt-20 h.t<-a2+a3*exp(b3*t) S.t<-exp(-a2*t+a3/b3*(1-exp(b3*t)))

Hello R users, I am trying to calculate the stochastic lambda for a published matrix population model using the popbio package. Unfortunately, I have been unable to match the published results. Can anyone tell me whether this is due to slightly different methods being used, or have I gone wrong somewhere in my code? Could the answer be as simple as comparing deterministic lambdas to