Normal and t-mixture distributions
Random generation and density values from normal and t-mixture distributions.
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)
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 |
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.
Normal and t-mixture random vectors and density values.
## 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)
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