ks (version 1.9.5)

mixt: Normal and t-mixture distributions

Description

Random generation and density values from normal and t-mixture distributions.

Usage

dnorm.mixt(x, mus=0, sigmas=1, props=1)
rnorm.mixt(n=100, mus=0, sigmas=1, props=1, mixt.label=FALSE)
dmvnorm.mixt(x, mus, Sigmas, props)
rmvnorm.mixt(n=100, mus=c(0,0), Sigmas=diag(2), props=1, mixt.label=FALSE)
rmvt.mixt(n=100, mus=c(0,0), Sigmas=diag(2), dfs=7, props=1)
dmvt.mixt(x, mus, Sigmas, dfs, props)

Arguments

n

number of random variates

x

matrix of quantiles

mus

(stacked) matrix of mean vectors (>1-d) or vector of means (1-d)

Sigmas

(stacked) matrix of variance matrices (>1-d)

sigmas

vector of standard deviations (1-d)

props

vector of mixing proportions

mixt.label

flag to output numeric label indicating mixture component. Default is FALSE.

dfs

vector of degrees of freedom

Value

• Normal and t-mixture random vectors and density values.

Details

rmvnorm.mixt and dmvnorm.mixt are based on the rmvnorm and dmvnorm functions from the mvtnorm package. Likewise for rmvt.mixt and dmvt.mixt.

Examples

## univariate normal mixture
x <- rnorm.mixt(1000, mus=c(-1,1), sigmas=c(0.5, 0.5), props=c(1/2, 1/2))

## bivariate mixtures 
mus <- rbind(c(-3/2,0), c(3/2,0))
Sigmas <- rbind(diag(c(1/16, 1)), rbind(c(1/16, 1/18), c(1/18, 1/16)))
props <- c(2/3, 1/3)
dfs <- c(7,3)
x <- rmvnorm.mixt(1000, mus=mus, Sigmas=Sigmas, props=props)
y <- rmvt.mixt(1000, mus=mus, Sigmas=Sigmas, dfs=dfs, props=props)