plot_class_allocation: Plot class allocation (for 'P_r = 2' only)

In RprobitB: Bayesian Probit Choice Modeling

Plot class allocation (for P_r = 2 only)

Description

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.

Usage

plot_class_allocation(beta, z, b, 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.

Details

Only applicable in the two-dimensional case, i.e. only if P_r = 2.

Value

No return value. Draws a plot to the current device.

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)

RprobitB documentation built on Nov. 10, 2022, 5:12 p.m.