mvrnorm.svd: Simulate from a Multivariate Normal, Poisson, Exponential, or...

In mvdalab: Multivariate Data Analysis Laboratory

Simulate from a Multivariate Normal, Poisson, Exponential, or Skewed Distribution

Description

Produces one or more samples from the specified multivariate distribution.

Usage

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 ([e-mail])

Examples

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 Oct. 6, 2022, 1:05 a.m.