similar to: How to use mvrnorm?

I want to generate multiple multivariate normal samples with different mean vectors and common covariance matrix. I can do this with a loop, but can't quite figure out how to do it with apply and friends. In the example below, I want values to have 3 columns: group, x, y # number of groups, and group means x <- jitter(seq(2,10,by=2)) y <- x + rnorm(length(x), 0, .5) means <-

Hi All This might be something very trivial but I seem to miss something in the syntax or logic which makes me keep wandering around the problem without arriving at a solution. What I want to do is to simulate a sample data for performing cluster analysis. I tried to use x1= mvrnorm(10,rep(0.8,3),diag(3)) x2= mvrnorm(10,rep(0,3),diag(3)) x3= mvrnorm(10,rep(-0.5,3),diag(3)) x=rbind(x1,x2,x3)

Hello, does anybody know another faster function for random multivariate normal variable simulation? I'm using mvrnorm, but as profiling shows, my algorithm spends approximately 50 % in executing mvrnorm function. Maybe some of you knows much faster function for multivariate normal simulation? I would be very gratefull for advices. -- View this message in context:

Hi all, Recently our statistics students noticed that their Gibbs samplers were crashing due to some NaNs in some parameters. The NaNs came from mvrnorm (Ripley & Venables' MASS package multivariate normal sampling function) and with some more investigation it turned out that they were generated by function eigen, the eigenvalue computing function. The problem did not seem to happen

I am trying to simulate draws from a multivariate normal using mvrnorm, and am getting the following error message: Error in mu + eS$vectors %*% diag(sqrt(pmax(ev, 0)), p) %*% t(X) : non-conformable arrays I do not understand why I am getting this message, since the vector of means I am giving to the function is 13 by 1 and the variance matrix I am giving to the function is 13

Dear List: I am generating N datasets using the following Sigma<-matrix(c(400,80,80,80,80,400,80,80,80,80,400,80,80,80,80,400),4,4 ) mu<-c(100,150,200,250) N=100 for(i in 1:N) { assign(paste("Data.", i, sep=''), as.data.frame(cbind(seq(1:1000),(mvrnorm(n=1000, mu, Sigma))))) } With these datasets, I need to work on some of the variables and then run each dataset

Dear All,   I am using the following commands to generate a given dataset:   a <-c(0.348,0.007,0.503,0.58,0.21) cov <-c(0.0448,0,0,0,0,0.0001,0.0001,0,0,0,-0.0055,-0.0005,0.0495,0,0,0.0218,0.0009,-0.0253,0.1103,0,-0.0102,-0.0007,0.00631,0.0067,0.0132) b <-matrix(cov,nrow=5, ncol = 5, byrow = TRUE,dimnames = NULL) g <-mvrnorm(10000,a,b)   is there a way to place limits on the simulated

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

Hi, I would like to create M paths of 2 correlated brownian motion incrementals where each path is of length N. I use mvrnorm to create all the increments, i.e. h <- 1.0; COV <- matrix(c(1,0,0,1),nrow=2); dW <- h * t(mvrnorm(n=N*M,mu=c(0,0),Sigma=COV)); The next step is that I'd like to wrap dW (2D matrix of size 2x(NM) into a 3D array where each slice is 2xN matrix and

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

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

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 all, I'm using the code below within a loop that I run thousands of times and even with the super-computing resources at my disposal this is just too slow. The snippet below takes about 10s on my machines, which is an order of magnitude or two slower than would be preferable; in the end I'd like to set the number of monte carlo experiments to 1e4 or even 1e5 to ensure stable

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

Hi, all! I'm new to R but need to use it to solve a little problem I'm having with a paper I'm writing. The question has a few components and I'd appreciate guidance on any of them. 1. The most essential thing is that I need to generate some multivariate normal data on a restricted integer range (1 to 7). I know I can use MASS mvrnorm command to do this but have a couple questions

Hi Nick, I think that the problem here is your use of $coef to extract the coefficients of the ridge regression. The help for lm.ridge states that coef is a "matrix of coefficients, one row for each value of lambda. Note that these are not on the original scale and are for use by the coef method." I ran a small test with simulated data, code is copied below, and indeed the output from

Dear list, I have data like this: dat1 <- data.frame(subject=rep(1:10,2), cond1=rep(c("A","B"),each=5), cond2=rep(c("C","D"),each=10), choice=sample(0:1,10,replace=TRUE)) I would like to compare subjects' "choice" for (cond1=="A" & cond2=="C") vs

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

Virt SIG folks, please join me in welcoming our latest SIG member, Marianne Lombard (username: jehane). She's been a Fedora contributor for quite sometime already, and will now be helping to ensure I'm not being a bottleneck RE: docker on CentOS virt :) Welcome aboard, Marianne. -- Lokesh Freenode: lsm5 GPG: 0xC7C3A0DD -------------- next part -------------- A non-text attachment was

I have two lists of the same shape, like this: x <- list() x[[1]] <- c("one","two") x[[2]] <- c("three","four","five") y <- list() y[[1]] <- c(TRUE,FALSE) y[[2]] <- c(FALSE,TRUE,TRUE) I would like to index x "by" y, that is, the result in this case should be: z [[1]] [1] "one" [[2]] [1] "four"