rmix {mixbox} | R Documentation |
Generating realization from finite mixture models.
Description
The density function of a restricted G
-component finite mixture model can be represented as
{\cal{M}}(\bold{y}|\bold{\Psi})=\sum_{g=1}^{G} \omega_{g} f_{\bold{Y}}(\bold{y}, \bold{\Theta}_g),
where positive constants \omega_{1}, \omega_{2},\cdots,\omega_{G}
are called weight (or mixing proportions) parameters with this properties that \sum_{g=1}^{G}\omega_{g}=1
and \bold{\Psi} = \bigl(\bold{\Theta}_{1},\cdots, \bold{\Theta}_{G}\bigr)^{\top}
with \bold{\Theta}_g=\bigl({\bold{\omega}}_g, {\bold{\mu}}_g, {{\Sigma}}_g, {\bold{\lambda}}_g\bigr)^{\top}
. Herein, f_{\bold{Y}}(\bold{y}, \bold{\Theta}_g)
accounts for the density function of random vector \bold{Y}
within g
-th component that admits the representation given by
{\bf{Y}} \mathop=\limits^d {\bold{\mu}}_{g}+\sqrt{W}{\bold{\lambda}}_{g}\vert{Z}_0\vert + \sqrt{W}{\Sigma}_{g}^{\frac{1}{2}} {\bf{Z}}_1,
where {\bold{\mu}}_{g} \in {R}^{d}
is location vector, {\bold{\lambda}}_{g} \in {R}^{d}
is skewness vector, and \Sigma_{g}
is a positive definite symmetric dispersion matrix for g=1,\cdots,G
. Further, W
is a positive random variable with mixing density function f_W(w| \bold{\theta}_{g})
, {Z}_0\sim N(0, 1)
, and {\bold{Z}}_1\sim N_{d}\bigl( {\bold{0}}, \Sigma_{g}\bigr)
. We note that W
, Z_0
, and {\bf{Z}}_1
are mutually independent.
Usage
rmix(n, G, weight, model = "restricted", mu, sigma, lambda, family = "constant",
theta = NULL)
Arguments
n |
number of realizations. |
G |
number of components. |
weight |
a vector of weight parameters (or mixing proportions). |
model |
It must be |
mu |
a list of location vectors of |
sigma |
a list of dispersion matrices of |
lambda |
a list of skewness vectors of |
family |
name of mixing distribution. By default |
theta |
a list of maximum likelihood estimator(s) for |
Value
a matrix with n
rows and d + 1
columns. The first d
columns constitute n
realizations from random vector \bold{Y}=(Y_1,\cdots,Y_d)^{\top}
and the last column is the label of realization \bold{Y}_i
( for i = 1, \cdots n
) indicating the component that \bold{Y}_i
is coming from.
Author(s)
Mahdi Teimouri
Examples
weight <- rep( 0.5, 2 )
mu1 <- rep(-5 , 2 )
mu2 <- rep( 5 , 2 )
sigma1 <- matrix( c( 0.4, -0.20, -0.20, 0.4 ), nrow = 2, ncol = 2 )
sigma2 <- matrix( c( 0.4, 0.10, 0.10, 0.4 ), nrow = 2, ncol = 2 )
lambda1 <- matrix( c( -4, -2, 2, 5 ), nrow = 2, ncol = 2 )
lambda2 <- matrix( c( 4, 2, -2, -5 ), nrow = 2, ncol = 2 )
theta1 <- c( 10, 10 )
theta2 <- c( 20, 20 )
mu <- list( mu1, mu2 )
sigma <- list( sigma1 , sigma2 )
lambda <- list( lambda1, lambda2)
theta <- list( theta1 , theta2 )
Y <- rmix( n = 100, G = 2, weight, model = "canonical", mu, sigma, lambda,
family = "igamma", theta )