mvrnorm.svd {mvdalab} | R Documentation |
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, |
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 (nelson.afanador@mvdalab.com)
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))
[Package mvdalab version 1.7 Index]