similar to: Converting 2D matrix to 3D array

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, [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

I try to do a test for dirichlet process for Multivariate normal, but Winbugs always says "expected multivariate node", does that mean I miss something at initialization? I will really appreciate the help to solve this problem Here is the R code, and Winbugs code. model { for(i in 1:N){ y[i,1:2] ~ dmnorm(mu[i,],tau[i,,]) S[i] ~ dcat(pi[]) mu[i,1:2] <- mu.star[S[i],]

R'istas: I am trying to plot a marginal distribution on the plane behind a persp() plot. My existing code is: library(MASS) X <- mvrnorm(1000,mu=c(0,0),Sigma=matrix(c(1,0,0,1),2)) X.kde <- kde2d(X[,1],X[,2],n=25) # X.kde is list: $x 1*n, $y 1*n, $z n*n persp(X.kde,phi=30,theta=60,xlab="x_b",ylab="x_a",zlab="f") ->res Any suggestions are very

Dear All, I would be very grateful for your help concerning the following question: Below mentioned programme is available on net to generate longitudinal data. Usually we get almost same parameter estimates as used to generate the data. The problem here is I am not able to get it for data used here, despite increasing sample size and number of simulations. Is it normal to expect this type of

Hi, I have thought a lot about my problem and also posted it on stackexchange, the post can be found here: http://stats.stackexchange.com/questions/44276/how-to-plot-3d-gbm but I have got no useful answer, that's why I trry the last possibility, which is to ask you professional guys. I want to recreate the following picture with my own code, the picture can be found in my other post:

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

I'm fitting generalized linear mixed models to using several fixed effects (main effects and a couple of interactions) and a grouping factor (site) to explain the variation in a dichotomous response variable (family=binomial). I wanted to compare the output I obtained using PROC GLIMMIX in SAS with that obtained using lmer in R (version 2.6.1 in Windows). When using lmer I'm specifying

Hi, i would to know, if someone have ever write the code to estimate the parameter (mixing proportion, mean, a var/cov matrix) of a mixture of two multivariate normal distribution. I wrote it and it works (it could find mean and mixing proportion, if I fix the var/cov matrix), while if I fix anything, it doesn't work. My suspect is that when the algorithm iterates the var/cov matrix, something

Hi, i would to know, if someone have ever write the code to estimate the parameter (mixing proportion, mean, a var/cov matrix) of a mixture of two multivariate normal distribution. I wrote it and it works (it could find mean and mixing proportion, if I fix the var/cov matrix), while if I fix anything, it doesn't work. My suspect is that when the algorithm iterates the var/cov matrix, something

Hard to help you if you don't provide a reproducible example. On Sun, Mar 4, 2018 at 1:05 PM, Christien Kerbert < christienkerbert at gmail.com> wrote: > d is the number of observed variables (d = 3 in this example). n is the > number of observations. > > 2018-03-04 11:30 GMT+01:00 Eric Berger <ericjberger at gmail.com>: > >> What is 'd'? What is

Dear R Helpers, I have searched for any R package or code for simulating multivariate fractional Brownian motion (mFBM) or multivariate fractional Gaussian noise (mFGN) when a covariance matrix are given. Unfortunately, I could not find such a package or code. Can you suggest any solution for multivariate FBM and FGN simulation? Thank you for your help. Best Regards, Ryan ----- Wonsang You

Hi list, I'm trying to generate fractional brownian surfaces in R. [A detailed description with respect to various techniques to generating these neutral landscapes has been described by Timothy Keitt (spectral representation of neutral landscapes, Landscape Ecology, 2000) and probably various other authours.] Are there any packages that deal with this or related items? I've been

> On Mar 3, 2018, at 3:04 PM, Christien Kerbert <christienkerbert at gmail.com> wrote: > > Dear list members, > > 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 <-

I would like to plot multiple random walks onto the same graph. My p variable dictates how may random walks there will be. par(mfrow=c(1,1)) p <- 100 N <- 1000 S0 <- 10 mu <- 0.03 sigma <- 0.2 nu <- mu-sigma^2/2 x <- matrix(rep(0,(N+1)*p),nrow=(N+1)) y <- matrix(rep(0,(N+1)*p),nrow=(N+1)) t<- (c(0:N))/N for (j in 1:p) { z <- rnorm(N,0,1) x[1,j] <- 0 y[1,j]

d is the number of observed variables (d = 3 in this example). n is the number of observations. 2018-03-04 11:30 GMT+01:00 Eric Berger <ericjberger at gmail.com>: > 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

Dear all, I have just uploaded to CRAN the version 1.6 of the package 'adehabitat'. Significant changes are listed below: * The package has been reorganized into four parts (see ?adehabitat-package for a description): (i) management of raster maps, (ii) habitat selection / ecological niche analysis, (iii) home range analysis, and (iv) analysis of animals trajects. The package contains

Dear all, I have just uploaded to CRAN the version 1.6 of the package 'adehabitat'. Significant changes are listed below: * The package has been reorganized into four parts (see ?adehabitat-package for a description): (i) management of raster maps, (ii) habitat selection / ecological niche analysis, (iii) home range analysis, and (iv) analysis of animals trajects. The package contains

Hi, I have been using maxLik to do some MLE of Geometric Brownian Motion Process and everything has been going fine, but know I have tried to do it with jumps. I have create a vector of jumps and then added this into my log-likelihood equation, know I am getting a message: NA in the initial gradient My codes is hear # n<-length(combinedlr) j<-c(1,2,3,4,5,6,7,8,9,10)

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 ones on the diagonal and rho = 0.15 on the non-diagonal. Then y