This function plots the allocation of decision-maker specific coefficient vectors
`beta`

given the allocation vector `z`

, the class means `b`

,
and the class covariance matrices `Omega`

.

## Arguments

- beta
The matrix of the decision-maker specific coefficient vectors of dimension

`P_r`

x`N`

. Set to`NA`

if`P_r = 0`

.- z
The vector of the allocation variables of length

`N`

. Set to`NA`

if`P_r = 0`

.- b
The matrix of class means as columns of dimension

`P_r`

x`C`

. Set to`NA`

if`P_r = 0`

.- Omega
The matrix of class covariance matrices as columns of dimension

`P_r*P_r`

x`C`

. Set to`NA`

if`P_r = 0`

.- ...
Optional visualization parameters:

`colors`

, a character vector of color specifications,`perc`

, a numeric between 0 and 1 to draw the`perc`

percentile ellipsoids for the underlying Gaussian distributions (`perc = 0.95`

per default),`r`

, the current iteration number of the Gibbs sampler to be displayed in the legend,`sleep`

, the number of seconds to pause after plotting.

## Examples

```
b <- matrix(c(-1,1,1,1), ncol = 2)
Omega <- matrix(c(0.8,0.5,0.5,1,0.5,-0.2,-0.2,0.3), ncol = 2)
z <- rep(1:2, each = 10)
beta <- sapply(z, function(z) rmvnorm(mu = b[,z], Sigma = matrix(Omega[,z], ncol = 2)))
RprobitB:::plot_class_allocation(beta = beta, z = z, b = b, Omega = Omega,
colors = c("red","blue"), perc = 0.5, r = 1)
```