search for: mvrnorm

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...

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) I would like use table() to see if the separation is wide apart such that the first 10 rows of x are clustered together. for eg: when I use table() and if I get an ouptut like 1...

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: http://r.789695.n4.nabble.com/faster-mvrnorm-alte...

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 when the option "symmetric" was set to FALSE...

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...

...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 <- cbind(x,y) Sigma <- matrix(c(6,3,3,2),2,2) # loop version n<- 10 values <- NULL for (i in 1:length(x)) { val <- mvrnorm(n, means[i,], .5*Sigma, empirical=TRUE) values <- rbind(values, val) } group <- factor(rep(letters[1:length(x)], each=n)) values <- cbind(group=group, values) > str(values) num [1:50, 1:3] 1 1 1 1 1 1 1 1 1 1 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$...

...owing 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 values so that they all remain positive? I am simulating biological variables that should never be 0.   thnaks,   Andras [[alternative HTML version deleted]]

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 through a linear model. I am having some trouble working with variables within the loop and wonder if anyone can offer any pointers. The first thing I am trying to do is add 2 variabl...

Dear R community! I would like to simulate distributions based on a polynomial model. It consists of 96 values and I want to randomly select new normaly distributed values around the modeled values. Furthermore, each value should be correlated with the previous one. Is it correct to therefore use mvrnorm? How do I define sigma? Thank you very much for your help. All the best, Marianne -- View this message in context: http://r.789695.n4.nabble.com/How-to-use-mvrnorm-tp3765225p3765225.html Sent from the R help mailing list archive at Nabble.com.

...2 components? library(MASS) > p0 <- seq(0.1, 0.9,by=0.1) > m <- 10 > > > p0 <- p0[7] > > ## data generation > > mzero <- p0*m > mone <- m-mzero > mu1 <- rnorm(mone, 0,1) > mu <- c(rep(0,mzero), mu1) > > var <- diag(m) > zv <- mvrnorm(n, mu, var) Error in mvrnorm(n, mu, var) : incompatible arguments > mone [1] 3 > mu1 [1] -0.08802108 0.77904786 [[alternative HTML version deleted]]

Hello. Somebody knows how can I generate a set of n random vectors of a normal distribution of several variables? For example, I want to generate n=100 random vectors of two dimensions for a normal with mean c(0,1) and variance matrix: matrix(c(2,1,1,3),2,2). Thanks in advance, Arnau.

...iable cannot vary from -infinity to +infinity, but are truncated and so are between two finite limits L1 and L2. Does R1.7 have this capability? If so what is (are) the relevant command(s)? 2) I am also interested in sampling from a multivariate normal distribution. I see that R has a command mvrnorm, but when I try to use it, I get a message to the effect that the command is not recognized. Is there something I must do to enable this command, or is it not available in version 1.7? Thanks, Howard [[alternate HTML version deleted]]

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 accurately the relationships between predictors. 2. To specify the coefficients of the regression of Y on predictors, I must specify separately the predictors for Y=1 and Y=0, I can vary the mean and...

Hello all. I'm building a package where I want to include a function from two different packages. In particular, I want to include mvrnorm and hyperg_2F1 from MASS and gsl, respectively (but the specific functions do not matter). With what I've tried after reading the "Specifying imports and exports" section from the "Writing R Extensions" manual, I get an error: "Namespace dependencies not required.&q...

...follow normal distributions (these effects can be correlated; below I've set their correlation to .5). I have an entire matrix of integers, and am converting those into two entire matrices of effects. #HOW I'VE DONE IT SO FAR To get the correlations between the effects, I've used the mvrnorm function from "MASS" To convert the allele names to genetic effects, I've created a function (make.effect) that resets the set.seed() to the allele name each time its called. To get the matrix of genetic effects, I use sapply. #THE PROBLEM The problem is that I often need to conve...

Thanks for your reply. I use mvrnorm from the *MASS* package and lmrob from the *robustbase* package. To further explain my data generating process, the idea is as follows. The explanatory variables are generated my a multivariate normal distribution where the covariance matrix of the variables is defined by Sigma in my code, with on...

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...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 more robust. I have seen conflicting (or at least seemingly conflicting to me, a relative statistics novice), views on whether one can use the correlation matrix with these commands instead of the covariance matrix. I though...

What is 'd'? What is 'n'? On Sun, Mar 4, 2018 at 12:14 PM, Christien Kerbert < christienkerbert at gmail.com> wrote: > Thanks for your reply. > > I use mvrnorm from the *MASS* package and lmrob from the *robustbase* > package. > > To further explain my data generating process, the idea is as follows. The > explanatory variables are generated my a multivariate normal distribution > where the covariance matrix of the variables is defined by S...

...embers, I want to perform an MM-regression. This seems an easy task using the function lmrob(), however, this function provides me with NA coefficients. My data generating process is as follows: rho <- 0.15 # low interdependency Sigma <- matrix(rho, d, d); diag(Sigma) <- 1 x.clean <- mvrnorm(n, rep(0,d), Sigma) beta <- c(1.0, 2.0, 3.0, 4.0) error <- rnorm(n = n, mean = 0, sd = 1) y <- as.data.frame(beta[1]*rep(1, n) + beta[2]*x.clean[,1] + beta[3]*x.clean[,2] + beta[4]*x.clean[,3] + error) xy.clean <- cbind(x.clean, y) colnames(xy.clean) <- c("x1", "x2&quo...