Last data update: 2014.03.03

R: The Multivariate Skew Normal Distribution
ddmsnR Documentation

The Multivariate Skew Normal Distribution

Description

Density and random generation for Multivariate Skew Normal distributions with mean vector mean, covariance matrix cov, and skew parameter vector del.

Usage

ddmsn(dat,n, p, mean, cov, del)
rdmsn(    n, p, mean, cov, del)

Arguments

dat

An n by p numeric matrix, the dataset

n

An integer, the number of observations

p

An integer, the dimension of data

mean

A length of p vector, the mean

cov

A p by p matrix, the covariance

del

A length of p vector, the skew parameter

Value

ddmsn gives the density values; rdmsn generates the random numbers

See Also

rdemmix,ddmvn,ddmvt, ddmst,rdmvn,rdmvt, rdmst.

Examples


n <- 100
p <- 2

mean <- rep(0,p)
cov  <- diag(p)
del<- c(0,1)

set.seed(3214)

y   <- rdmsn(  n,p,mean,cov,del)

den <- ddmsn(y,n,p,mean,cov,del)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(EMMIXskew)
Loading required package: lattice
Loading required package: mvtnorm
Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/EMMIXskew/ddmsn.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ddmsn
> ### Title: The Multivariate Skew Normal Distribution
> ### Aliases: ddmsn rdmsn
> ### Keywords: cluster datasets
> 
> ### ** Examples
> 
> 
> n <- 100
> p <- 2
> 
> mean <- rep(0,p)
> cov  <- diag(p)
> del<- c(0,1)
> 
> set.seed(3214)
> 
> y   <- rdmsn(  n,p,mean,cov,del)
> 
> den <- ddmsn(y,n,p,mean,cov,del)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>