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)
)
#>            [,1]       [,2]
#> [1,] 0.84055535  1.0005869
#> [2,] 0.07520765 -0.6248856
#> [3,] 0.07520765 -0.6248856
#> [4,] 0.33410723  0.9020809