similar to: Bootstrapping a covariance matrix

I have the following function, a MSE calc based on some Multivariate normals: MV.MSE<-function(n,EP,X,S){ (dmvnorm(X,mean=rep(0,2),I+S+EP)-dmvnorm(X,mean=rep(0,2),I+S))^2 + 1/n*(dmvnorm(X,mean=rep(0,2),1+S+EP/2)*det(4*pi*EP)^-.5- (dmvnorm(X,mean=rep(0,2),I+S+EP ))^2)} I can get the MV.MSE for given values of the function e.g

Hi, I want to simulate a data set with similar covariance structure as my observed data, and have calculated a covariance matrix (dimensions 8368*8368). So far I've tried two approaches to simulating data: rmvnorm from the mvtnorm package, and by using the Cholesky decomposition (http://www.cerebralmastication.com/2010/09/cholesk-post-on-correlated-random-normal-generation/). The problem is

Dear All, For function: rmvnorm{mvtnorm} in (library mvtnorm, not splus2R), if I generate 2 bivariate normal samples as follows: > rmvnorm(2,mean=rep(0,2),sigma=diag(2)) [,1] [,2] [1,] 2.0749459 1.4932752 [2,] -0.9886333 0.3832266 Where is the first sample, it is stored in the first row or the first column? Does this function store samples row-wise or column-wise? Thank

This continues the message "data.frame within a function (PR#9294)" that was posted on 2006/10/12. Duncan Murdoch kindly replied. I'm using the current version R 2.4.0, but the same issue exists. Just copy and paste the following code under R, and compare the output of f1() and f2() and the output of f3() and f4(). Does anybody have any idea? Thanks.

Hi, My R version is 2.4.1 and I installed the the packages MASS and run command library("MASS"), however when I type ?rmvnorm, no help topic found, it worked before. I tried to ype ?rinvgamma from "MCMCpack" which works great. Anybody have idea? I also reinstalled MASS package, but when I try to type rmvnorm(), no functions found. Pat

Hi R-fellows, I am trying to simulate a multivariate correlated sample via the Gaussian copula method. One variable is a binary variable, that should be autocorrelated. The autocorrelation should be rho = 0.2. Furthermore, the overall probability to get either outcome of the binary variable should be 0.5. Below you can see the R code (I use for simplicity a diagonal matrix in rmvnorm even if it

Dear All I want to generate multivariate normal data in R for a given covariance matrix, i.e. my generated data must have the given covariance matrix. I know the rmvnorm command is to be used but may be I am failing to properly assign the covariance matrix. Any help will be greatly appreciated thanks. M. R. Ahmad

This might not be the correct forum for this question for there might be some flaws in my logic so the R function I'm looking for might not be the correct, but I know there?s a lot of smart people in this forum so please correct me if I'm wrong. I have been googling and searching in this forum for something useful but so far I'm out of luck. This is the background to my problem. I

Hi, How can I generate a random signal that's correlated with a given signal at a given correlation (say 0.7)? I've been looking at rmvnorm etc but don't seem to figure it out. Thanks ----- Yasir H. Kaheil Columbia University -- View this message in context: http://www.nabble.com/generating-a-random-signal-with-a-known-correlation-tp18932541p18932541.html Sent from the R help

Dear R sages, I used the function rbind to combine a matrix (M) and a vector (Fert) to get a new matrix (A). This was fine. The issue is however, that the new matrix A has as its row names the name of the vector Fert, even though I set teh new vector A to have dimnames=NULL. See short code below fyi. *Fert<-c(0,1,5) * *M <- matrix(0, 2, 3) diag(M) <- c(0.3,0.5) * *A<-

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

Hi everybody, I am trying to generate two random walks with an specific correlation, for example, two random walks of 200 time steps with a correlation 0.7. I built the random walks with: x<-cumsum(rnorm(200, mean=0,sd=1)) y<-cumsum(rnorm(200, mean=0,sd=1)) but I don't know how to fix the correlation between them. With white noise is easy to fix the correlation using the function

 Dear R-helpers, My question is related to how to impose constraints when when sampling from a distribution. For example, suppose I'm sampling a vector from a multivariate normal distribution vbeta <- 100*diag(2) mbeta <- c(1,1) ans <- beta <- c(rmvnorm(1,mbeta,vbeta)) ans will thus be a vector with two elements. My question is how do I place a restriction on one of the

Hello, I want to create two random variables (x1,x2) both with uniform distribution bounded by (-1) and (1) that has a correlation of 0.6 between them. Does somebody know how I can do it? For normal random variables I known how to implement it with the rmvnorm command but I don't know how to do it with variables uniformly distributed. Thanks a lot. Alexandra [[alternative HTML

Hi, I have great difficulty in simulation the a dataset based in a loading matrix [c(1,1,1,2,3,3,3,4,4,3,2,2,1,1), 7, 2) and an error covariance matrix is 2*I. I have to simulate a dataset with 7 variables and 50 rows. I search a lot and did find some information on this, for example, using rmvnorm(). But I could do it. Please help! -- View this message in context:

Dear sir, I would ask if there are in R some code to generate a random sample from a mvariate student distribution like that one wich generate the multivariate normal one i mean( rmvnorm(n, mu, sigma) Second question : if R can plot density 3Dcurve I don't mean de histogram but de hole density function(normal for example). I use a windows version of The R software Thank you in advance wiyh

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

R-Help I am attempting to create a series of bivariate normal distributions. So using the mvtnorm library I have created the following code ... # Standard deviations and correlation sig_x <- 1 sig_y <- 1 rho_xy <- 0.0 # Covariance between X and Y sig_xy <- rho_xy * sig_x *sig_y # Covariance matrix Sigma_xy <- matrix(c(sig_x ^ 2, sig_xy, sig_xy, sig_y ^ 2), nrow = 2, ncol = 2)

Dear all, I would like to generate bi-variate normal data given that the first column of the data is known. for example: I first generate a set of data using the command, x <- rmvnorm(10, c(0, 0), matrix(c(1, 0, 0, 1), 2)) then I would like to sum up the two columns of x: x.sum <- apply(x, 1, sum) now with x.sum I would like to generate another column of data, say y, that makes

library(mvtnorm) x = seq(-4,4,length=201) xy = expand.grid(x,x) sigma = (diag(c(1,1))+1)/2 d2 = matrix(dmvnorm(xy,sigma=sigma),201) xsamp = rmvnorm(200,sigma=sigma) contour(x,x,d2) points(xsamp,col=3,pch=16) pdf("pdftry.pdf") contour(x,x,d2) points(xsamp,col=3,pch=16) dev.off() postscript("pstry.ps") contour(x,x,d2) points(xsamp,col=3,pch=16) dev.off() # I can see