similar to: Simulating correlated distributions

A very basic question: How can I make R read some S+ files that I had originally. The source command says "file format unknown" Thanks Coomaren ........................................................................ ........................................................ Dr Coomaren P Vencatasawmy Senior Research Fellow Spatial Modelling Centre (SMC) P.O. Box 839 S-981 28 Kiruna

Hi, I would like to simulate data with a binary outcome and a set of predictors that are correlated. I want to be able to fix the number of event (Y=1) vs. non-event (Y=0). Thus, I fix this and then simulate the predictors. I have 2 questions: 1. When the predictors are continuous, I can use mvrnorm(). However, if I have continuous, ordinal and binary predictors, I'm not sure how to simulate

Hello there, I would like to simulate X --Normal (20, 5) Y-- Normal (40, 10) and the correlation between X and Y is 0.6. How do I do it in R? Thank you very much ----- TO GET MORE DETAILS CLICK HERE -- View this message in context: http://r.789695.n4.nabble.com/How-to-simulate-correlated-data-tp4651667.html Sent from the R help mailing list archive at

Dear List: I have created some code to simulate data from a complex sample where 5000 students are nested in 50 schools. My code returns a dataframe with a variable representing student achievement at a single time point. My actual code for creating this is below. What I would like to do is generate a second column of data that is correlated with the first at .8 and has the same means within

Hello there, I would like to simulate X --Normal (20, 5) Y-- Normal (40, 10) and the correlation between X and Y is 0.6. How do I do it in R? Thank you very much Lisa Wang Msc. Princess Margaret Hospital Toronto, Ca

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

Hi there, I would like to draw 10 correlations from a bivariate population - but every draw should be done with a different sample size. I thought I could to this with a loop: r=numeric(10) #Goal vector N = c(1000,100,80,250,125,375,90,211,160,540) #Sample size vector for(i in 1:10) { data <- mvrnorm(n=N,mu=c(0,0),Sigma=matrix(c(1,.3,.3,1),2)) r[i] <- cor(data[,1],data[,2]) }

Dear R users, I’m trying to understand how correlated predictors impact the Relative Importance measure in Stochastic Boosting Trees (J. Friedman). As Friedman described “ …with single decision trees (referring to Brieman’s CART algorithm), the relative importance measure is augmented by a strategy involving surrogate splits intended to uncover the masking of influential variables by others

Hi. I am trying to generate a correlated binary data set. I've tried to use mvtBinaryEP, binarySimCLF, and bindata packages but none of them works in R version 2.15.1. Do you know any package to generate correlated binary covariates and work in R version 2.15.1, or how to generate it? Thanks, [[alternative HTML version deleted]]

Hi all, I understand that it is simple to create data with a specific correlation (say, .5) using mvrnorm from the MASS library: > library(MASS) > set.seed(1) > > a=mvrnorm( + n=10 + ,mu=rep(0,2) + ,Sigma=matrix(c(1,.5,.5,1),2,2) + ,empirical=T + ) > a [,1] [,2] [1,] -1.0008380 -1.233467875 [2,] -0.1588633 -0.003410001 [3,] 1.2054727 -0.620558768

Hi,I want to generate a dataset, which have more than one clusters (say 2) and the elements in each cluster have high correlation (say 0.85) andelements among different clusters have low (say 0.1) or zero correlation.The correlation structure of final dataset should have a block-diagonal structure, that likes 0.85 0.85 .. 0.85 0 0 0 0 ...0 0 0...0 0 0

Dear R users I'd like to generate two sets of random numbers with a fixed correlation coefficient, say .4, using R. Any suggestion will be greatly appreciated. Regards, Kathryn Lord -- View this message in context: http://r.789695.n4.nabble.com/generate-two-sets-of-random-numbers-that-are-correlated-tp3735695p3735695.html Sent from the R help mailing list archive at Nabble.com.

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

Hi, I have a correlation matrix of about 3000 items, i.e., a 3000*3000 matrix. For each of the 3000 items, I want to get the top 50 items that have the highest correlation with it (excluding itself) and generate a data frame with 3 columns like ("ID", "ID2", "cor"), where ID is those 3000 items each repeat 50 times, and ID2 is the top 50 correlated items with ID,

Hi, I have just installed R 2.4.0 and when I try to load SpareseM, I get the following error message library(SparseM) Package SparseM (0.71) loaded. To cite, see citation("SparseM") Error in loadNamespace(package, c(which.lib.loc, lib.loc), keep.source = keep.source) : in 'SparseM' methods specified for export, but none defined: as.matrix.csr, as.matrix.csc,

[Apologies in advance if this is too "statistics" and not enough "R".] I've got an experiment with two sets of treatments. Each subject either received all treatments from set A or all treatments from set B. I can compute the N pairwise correlations for all treatments in either set using cor(). If I take the mean of these N pairwise correlations, I see that the effects

Dear R users I use simulated data to evaluate a model by sampling the parameters in my model from lognormal distributions. I would like these (lognormal distributed) parameters to be correlated, that is, I would like to have pairwise samples of 2 parameters with a given correlation coefficient. I have seen that a covariance matrix can be fixed when generating random variables from a

Dear everyone, I am having problem simulating multivariate data. Though I was able to simulate the data, but finding the variance-covariance matrix of simulated data did not give exact covariance matrix used in simulating the data. Unlike some other packages, like stata, using command "corr2data" will simulate data having the covariance matrix exactly with the specified covariance

Sir, I am working on fitting distribution on multivariate financial data and then simulate observations from that fitted distribution. I use stepAIC.ghyp() function of 'ghyp' library which select the best fitted distribution from generalized hyperbolic distribution class on the given dataset. data(indices) # Multivariate case: aic.mv <- stepAIC.ghyp(indices, dist =

Hi all, [this is a bit hard to describe, so if my initial description is confusing, please try running my code below] #WHAT I'M TRYING TO DO I'd appreciate any help in trying to speed up some code. I've written a script that converts a matrix of integers (usually between 1-10,000 - these represent allele names) into two new matrices of normally distributed values (representing