similar to: Sampling 1000 times from a bivariate normal distibution

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)

Dear All I want to generate variable with Bivariate Normal Distribution by use mean1 = a, variance1 = b, mean2 = c, variance2 = d, rho = e. How I can do this. Many Thanks. IRD [[alternative HTML version deleted]]

Dear All, For function: rmvnorm{mvtnorm} in (library mvtnorm, not splus2R), if I generate 2 bivariate normal samples as follows: > rmvnorm(2,mean=rep(0,2),sigma=diag(2)) [,1] [,2] [1,] 2.0749459 1.4932752 [2,] -0.9886333 0.3832266 Where is the first sample, it is stored in the first row or the first column? Does this function store samples row-wise or column-wise? Thank

Dear list members I need a function that calculates the bivariate normal distribution for each observation. It is part of a likelihood function and I have 1000's of cases. As I understand it I cannot use packages like "mvtnorm" because it requres a covariance matrix of the same dimension as the number of observations. Basically what I need is a function that takes as arguments a

Hi, there. Does anyone know the R function for calculating the cdf of bivariate normal distribution function? Thanks. Yulei [[alternative HTML version deleted]]

Package mvtnorm provides dmvnorm, pmvnorm that can be used to compute Pr(X=x,Y=y) and Pr(X<x,Y<y) for a bivariate normal. Are there functions that would compute Pr(X<x,Y=y)? I'm currently using "integrate" with dmvnorm but it is too slow.

Dear all R users, Please forgive me if my question is too trivial. Suppose I have two variables, (x,y) which is log-normally distributed with expected value (mu1, mu2) and some variance-covariance matrix. Now I want to draw a random sample of size 1000 from this distribution. Is there any function available to do this? Thanks and regards, Megh

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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

Dear R-List: I`m trying to calculate the probabilities for a bivariate normal distribution while using the mvtnorm-package(dmvnorm) and the adapt-package for multidimensional integration. The problem is that I can`t specify the upper bound in the adapt-package the way I need it because I don`t need a rectangular area. I want to calculate the probability starting at the origin under the line y=x.

-- R Help List -- I am looking for a bivariate normal cdf routine in R. I have some fortran routines for this, which appear to be based on 15-point quadrature. Any guidance/suggestions on making these in loadable R-functions would be appreciated. Thanks, Dan =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Daniel A. Powers, Ph.D. Department of Sociology University of Texas at Austin

sorry for cross-posting Dear all, I have tow (several) bivariate distributions with a known mean and variance-covariance structure (hence a known density function) that I would like to compare in order to get an intersect that tells me something about "how different" these distributions are (as t-statistics for univariate distributions). In order to visualize what I mean hear a little

Dear all, I would like to generate bi-variate normal data given that the first column of the data is known. for example: I first generate a set of data using the command, x <- rmvnorm(10, c(0, 0), matrix(c(1, 0, 0, 1), 2)) then I would like to sum up the two columns of x: x.sum <- apply(x, 1, sum) now with x.sum I would like to generate another column of data, say y, that makes

Hi, Does anyone know how to write a R function to solve the quantile c for the following equation. P(Z1>1.975, Z2<c)+ P(Z1>c, Z2>c)=0.05/6. Z1 and Z2 have a bivariate normal distribution with mean 0, variance 1 and correlation 0.5. Thanks a lot! Hannah [[alternative HTML version deleted]]

Dear sir, I would ask if there are in R some code to generate a random sample from a mvariate student distribution like that one wich generate the multivariate normal one i mean( rmvnorm(n, mu, sigma) Second question : if R can plot density 3Dcurve I don't mean de histogram but de hole density function(normal for example). I use a windows version of The R software Thank you in advance wiyh

Dear all, Y, X are bivariate normal with all the parameters known. I would like to generate numbers from the distribution Y | X > c where c is a constant. Does there exist an R function generating random numbers from such a distribution? Thank you very much. hannah [[alternative HTML version deleted]]

I tried to download Digium’s Opus Codec via the following link, but the server is unavailable: http://downloads.digium.com/pub/telephony/codec_opus/ It took me a while to figure this out, because initially I tried downloading via selecting the Opus codec in make menuselect and realizing that it isn’t there after make install step. Can someone from Digium/Sangoma please confirm? FLORIAN

 Dear R-helpers, My question is related to how to impose constraints when when sampling from a distribution. For example, suppose I'm sampling a vector from a multivariate normal distribution vbeta <- 100*diag(2) mbeta <- c(1,1) ans <- beta <- c(rmvnorm(1,mbeta,vbeta)) ans will thus be a vector with two elements. My question is how do I place a restriction on one of the

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Hi, i have still the same problem. I use 3.0RC3 with lib/charcnv.c of the actual CVS (Sep 10 15:19). I works not realy: I can create (Windows XP + Explorer) the filename "L?sung.txt" but not "?sung.txt". -- Greetings M. Ungermann