# mvrnorm.svd: Simulate from a Multivariate Normal, Poisson, Exponential, or... In mvdalab: Multivariate Data Analysis Laboratory

## Description

Produces one or more samples from the specified multivariate distribution.

## Usage

 ```1 2 3``` ```mvrnorm.svd(n = 1, mu = NULL, Sigma = NULL, tol = 1e-06, empirical = FALSE, Dist = "normal", skew = 5, skew.mean = 0, skew.sd = 1, poisson.mean = 5) ```

## Arguments

 `n` the number of samples required. `mu` a vector giving the means of the variables. `Sigma` a positive-definite symmetric matrix specifying the covariance matrix of the variables. `tol` tolerance (relative to largest variance) for numerical lack of positive-definiteness in Sigma. `empirical` logical. If true, `mu` and `Sigma` specify the empirical not population mean and covariance matrix. `Dist` desired distribution. `skew` amount of skew for skewed distributions. `skew.mean` mean for skewed distribution. `skew.sd` standard deviation for skewed distribution. `poisson.mean` mean for poisson distribution.

## Details

`"mvrnorm.svd"` The matrix decomposition is done via `svd`

## Author(s)

Nelson Lee Afanador ([email protected])

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```Sigma <- matrix(c(1, .5, .5, .5, 1, .5, .5, .5, 1), 3, 3) Means <- rep(0, 3) Sim.dat.norm <- mvrnorm.svd(n = 1000, Means, Sigma, Dist = "normal") plot(as.data.frame(Sim.dat.norm)) Sim.dat.pois <- mvrnorm.svd(n = 1000, Means, Sigma, Dist = "poisson") plot(as.data.frame(Sim.dat.pois)) Sim.dat.exp <- mvrnorm.svd(n = 1000, Means, Sigma, Dist = "exp") plot(as.data.frame(Sim.dat.exp)) Sim.dat.skew <- mvrnorm.svd(n = 1000, Means, Sigma, Dist = "skewnorm") plot(as.data.frame(Sim.dat.skew)) ```

mvdalab documentation built on Nov. 17, 2017, 6 a.m.