update_Omega: Update class covariances
In RprobitB: Bayesian Probit Choice Modeling

update_Omega

R Documentation

Update class covariances

Description

Update class covariances

Usage

update_Omega(beta, b, z, m, n_Omega_0, V_Omega_0)

Arguments

`beta`	[`matrix(nrow = P_r, ncol = N)`] The matrix of the decider-specific coefficient vectors.
`b`	[`matrix(nrow = P_r, ncol = C)`] The matrix of class means as columns.
`z`	[`numeric(N)`] The decider class allocations.
`m`	[`numeric(C)`] The vector of current class frequencies.
`n_Omega_0`	[`integer(1)`] The degrees of freedom of the Inverse Wishart prior for each `Omega_c`.
`V_Omega_0`	[`matrix(P_r, P_r)`] The scale matrix of the Inverse Wishart prior for each `Omega_c`.

Value

A matrix of updated covariance matrices for each class in columns.

Examples

N <- 100
b <- cbind(c(0, 0), c(1, 1))
Omega <- matrix(c(1, 0.3, 0.3, 0.5, 1, -0.3, -0.3, 0.8), ncol = 2)
z <- c(rep(1, N / 2), rep(2, N / 2))
m <- as.numeric(table(z))
beta <- sapply(
  z, function(z) oeli::rmvnorm(n = 1, b[, z], matrix(Omega[, z], 2, 2))
)
update_Omega(
  beta = beta, b = b, z = z, m = m,
  n_Omega_0 = 4, V_Omega_0 = diag(2)
)

RprobitB documentation built on Aug. 26, 2025, 1:08 a.m.