mixt: Normal and t-mixture distributions

In ks: Kernel Smoothing

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=1, verbose=FALSE)
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)
mvnorm.mixt.mode(mus, Sigmas, props=1, verbose=FALSE)

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.
verbose	flag to print out progress information. Default is FALSE.
dfs	vector of degrees of freedom

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.

For the normal mixture densities, mvnorm.mixt.mode computes the local modes: these are usually very close but not exactly equal to the component means.

Value

Normal and t-mixture random vectors and density values.

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(-1,0), c(1, 2/sqrt(3)), c(1,-2/sqrt(3)))
Sigmas <- 1/25*rbind(invvech(c(9, 63/10, 49/4)), invvech(c(9,0,49/4)), invvech(c(9,0,49/4)))
props <- c(3,3,1)/7
dfs <- c(7,3,2)
x <- rmvnorm.mixt(1000, mus=mus, Sigmas=Sigmas, props=props)
y <- rmvt.mixt(1000, mus=mus, Sigmas=Sigmas, dfs=dfs, props=props)

mvnorm.mixt.mode(mus=mus, Sigmas=Sigmas, props=props)

ks documentation built on Aug. 11, 2023, 1:10 a.m.