similar to: Calculating Variance-covariance matrix for a multivariate normal distribution.

Hello everyone Is there a function/command to simulate from "matrix variate normal distribution" in R. A follow up question would be is there a function/command to obtain the density, distribution and quantile function of "matrix variate normal distribution" in R. Wikipedia has a good description of "matrix variate normal distribution" which is also alternatively

Hello, I have been googling for 2 days and I cannot find the answer in previous posts. I have a set of d-dimensional data elements (d=11 .. 14), each data point can be censored at different values both Lower-limit and upper limit. N = 2000 sets of vectors of D=11 data points per vector. Each of the N*D points can have different upper and lower limits. I "simply" want to fit a

Dear all, Does this make any sense: var() = cov() != acf(lag.max=0, type="covariance")? I have daily data of IBM for May 2005, and I'm using the logarithmic return: > ibm200505$LRAdj.Close [1] NA 0.0203152 0.0005508 -0.0148397 -0.0025182 0.0092025 -0.0013889 [8] 0.0098196 -0.0103757 -0.0274917 0.0005716 -0.0159842 -0.0074306 0.0091710 [15] 0.0002898 0.0226306

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

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

Hello R list subscribers, I am trying to use the "by" command to create line-specific variance covariance matrices (where "x" is the original data matrix): by(x, x$line, function(d) { d.clean <- d[,-1]}) write.table(d.clean$line[1,1], sep = ",", file = "covariances.csv", col.names = FALSE, row.names = FALSE, append = TRUE) write.table("", sep

Full_Name: Christian Lederer Version: 1.8.0 OS: Linux Submission from: (NULL) (217.229.7.13) R-1.8.0 seems to calculate wrong covariances, when the argument of cov() is a matrix or a data frame. The following should produce a matrix of zeroes and NaNs: x <- matrix(c(NA ,NA ,0.9068995 ,NA ,-0.3116229, -0.06011117 ,0.7310134 ,NA ,1.738362 ,0.6276125, 0.6615581 ,NA

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

Dear R-help, I would like to compute the variance for the proportion of treatment effect by a surrogate in a survival model (Lin, Fleming, and De Gruttola 1997 in Statistics in Medicine). The paper mentioned that the covariance matrix matches that of the covariance matrix estimator for the marginal hazard modelling of multiple events data (Wei, Lin, and Weissfeld 1989 JASA), and is implemented

Dear all R users, Please forgive me if my question is too trivial. Suppose I have two variables, (x,y) which is log-normally distributed with expected value (mu1, mu2) and some variance-covariance matrix. Now I want to draw a random sample of size 1000 from this distribution. Is there any function available to do this? Thanks and regards, Megh

Hallo! I want to understand / recalculate what is done to get the CI of the logistic regression evaluated with lrm. As far as I came back, my problem is the variance-covariance matrix fit$var of the fit (fit<-lrm(...), fit$var). Here what I found and where I stucked: ----------------- library(Design) # data D<-c(rep("a", 20), rep("b", 20)) V<-0.25*(1:40) V[1]<-25

I'm having trouble getting the relaimpo package to use a covariance matrix as input. I'm getting an error message that reads as follows: Error in eval(m$weights, data, parent.frame()) : numeric 'envir' arg not of length one I'm guessing there is something wrong with the structure of my covariance matrix, but it looks fine to me. Pardon my R ignorance if this is an easy

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 Folks, I want to do multivariate regression in R, i.e. basically (but with a complication -- see below): given an Nxp matrix Y of p-variate responses, and an Nxk matrix X of covariates, to fit the model Y = X*B + e with estimation of the kxp matrix of coefficients B and estimation of the pxp matrix of covariances between the p variates in Y. I haven't managed to find a

R-1.8.0 seems to calculate wrong covariances, when the argument of cov() is a matrix or a data frame. The following should produce a matrix of zeroes and NaNs: x <- matrix(c(NA ,NA ,0.9068995 ,NA ,-0.3116229, -0.06011117 ,0.7310134 ,NA ,1.738362 ,0.6276125, 0.6615581 ,NA ,NA ,-2.646011 ,-2.126105, NA ,1.081825 ,NA ,1.253795 ,1.520708,

Hello, I'm using dmnorm from the package {mnormt} and getting strange results. First, according to the documentation, dmnorm should return a vector of densities, and I'm only getting one value returned (which is what I would expect). I've been interpreting this as the joint density of all values in the x vector (which is what I want). Should a vector of densities be returned, and if

Dear all, I have been looking for a while for ways to simulate from Langevin distributions and I thought I would ask here. I am ok with finding an algorithmic reference, though of course, a R package would be stupendous! Btw, just to clarify, the Langevin distribution with (mu, K), where mu is a vector and K>0 the concentration parameter is defined to be: f(x) = exp(K*mu'x) / const where

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

While I don't know anything about Box's M test, I googled around and found a Matlab M-file that computes it. Below is my straight-forward translation of the code, without knowing Matlab or the formula (and done in a few minutes). I hope this demonstrates one of Prof. Ripley's point: If you really want to shoot yourself in the foot, you can probably program R to do that for you. [BTW:

I wish to generate 100 by 1 vector of x1 and x2 both are uniform distributed with covariance matrix \Sigma. Thanks, Michael [[alternative HTML version deleted]]