similar to: Compute a correlation matrix from an existing covariance matrix

Dear All, I have written the following programs to find a non-singular (10*10) covariance matrix. Here is the program: nitems <- 10 x <- array(rnorm(5*nitems,3,3), c(5,nitems)) sigma <- t(x)%*%x inverse <- try(solve(sigma), TRUE) while(inherits(inverse, "try-error")) { x <- array(rnorm(5*nitems,3,3), c(5,nitems)) sigma <- t(x)%*%x inverse <-

Suppose I have a Variance-covariance matrix A. Is there any fast way to calculate correlation matrix from 'A' and vice-versa without emplying any 'for' loop? [[alternative HTML version deleted]]

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

List members, Via searches I've seen similar discussion of this topic but have not seen resolution of the particular issue I am experiencing. If my search on this topic failed, I apologize for the redundancy. I am attempting to generate random covariance matrices but would like the corresponding correlations to be uniformly distributed between -1 and 1. The approach I have been using is:

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

Hello all (especially MCLUS users). I'm trying to make use of the MCLUST package by C. Fraley and A. Raftery. My problem is trying to figure out how the (model) identifier (e.g, EII, VII, VVI, etc.) relates to the covariance matrix. The parameterization of the covariance matrix makes use of the method of decomposition in Banfield and Rraftery (1993) and Fraley and Raftery (2002) where

Dear All,   is there a function in R that would help me convert a covariance matrix built based on arithmetic returns to a covariance matrix from log-returns?   As an example of the means and covariance from arithmetic:   mu <-c(0.094,0.006,1.337,1.046,0.263) sigma

Hi All. I need to create a first-order autoregressive covariance matrix (AR(1)) for a longitudinal mixed-model simulation. I can do this using nested "for" loops but I'm trying to improve my R coding proficiency and am curious how it might be done in a more elegant manner. To be clear, if there are 5 time points then the AR(1) matrix is 5x5 where the diagonal is a constant

Dear R users, I fitted a repeated measure model without random effects by using lme. I will use the estimates from that model as an initial estimates to do multiple imputation for missing values of the response variable in the model. I am trying to extract the within group correlation matrix or covariance matrix. here is my code: f = lme(y ~x0+x1+trt+tim+x1:tim +tim:trt,random=~-1|subj,

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)

I do have some trouble with matrices. I want to build up a covariance matrix with a hierarchical structure). For instance, in dimension n=10, I have two subgroups (called REGION). NR=2; n=10 CORRELATION=matrix(c(0.4,-0.25, -0.25,0.3),NR,NR) REGION=sample(1:NR,size=n,replace=TRUE) R1=REGION%*%t(rep(1,n)) R2=rep(1,n)%*%t(REGION) SIGMA=matrix(NA,n,n) for(i in 1:NR){ for(j in

I have a data matrix X (n x k, say) each row of which constitutes an observation of a k-dimensional random variable which I am willing, if not happy, to assume to be Gaussian, with mean ``mu'' and covariance matrix ``Sigma''. Distinct rows of X may be assumed to correspond to independent realizations of this random variable. Most rows of X (all but 240 out of 6000+ rows)

Dear useRs, I have written a function that implements a Bayesian method to compare a patient's score on two tasks with that of a small control group, as described in Crawford, J. and Garthwaite, P. (2007). Comparison of a single case to a control or normative sample in neuropsychology: Development of a bayesian approach. Cognitive Neuropsychology, 24(4):343?372. The function (see

Dear R community, I have trouble implementing a nested variance-covariance matrix in the lme function. The model has two fixed effects called End and logpgc, the response variable is the logarithm to base 2 of Intensity ( log2(Intensity) ) and the random effects are called Probe and ProbeNo. The model has the following nesting structure: A Pixel is nested within the ProbeNo,the ProbeNo is

Dear All, I would be grateful if you can help me. My problem is the following: I have a data set like: ID time X1 X2 1 1 x111 x211 1 2 x112 x212 2 1 x121 x221 2 2 x122 x222 2 3 x123 x223 where X1 and X2 are 2 covariates and "time" is the time of observation and ID indicates the

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

Ok, Thanks foe the answer, Ken: *1L, 2L etc are integers. (That is, identical to as.integer(1) , as.integer(2) etc) Using integers (instead of "numeric" type) is more efficient as here they're used as indexes and would be converted to integer anyway. Compare > is(1) ... and > is(1L) 1L:p is the sequence 1, 2, 3, ..., p (just like 1:p) Just for curiosity, what is

Greetings, I have a seemingly simple task which I have not been able to solve today and I checked all of the help archives on this and have been unable to find anything useful. I want to construct a symmetric matrix of arbtriray size w/o using loops. The following I thought would do it: p <- 6 Rmat <- diag(p) dat.cor <- rnorm(p*(p-1)/2) Rmat[outer(1:p, 1:p, "<")] <-

Hi, At present, i have two distinct and real values for the coefficient, which is  required in AR(2) model. Based on my revision, for distinct and real values of the coefficients in AR(2) model, the correlation structure separated by lag h can be computed by p(h) = a*z1^(-h) + b*z2^(h), where p(h) is the autocorrelation separated by lag h, a and b can be determined by initial values, z1 and z2

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