Dirichlet process class updates

Usage

update_classes_dp(
  beta,
  z,
  b,
  Omega,
  delta,
  mu_b_0,
  Sigma_b_0,
  n_Omega_0,
  V_Omega_0,
  identify_classes = FALSE,
  Cmax = 10L
)

Arguments

beta: [matrix(nrow = P_r, ncol = N)]
The matrix of the decider-specific coefficient vectors.
z: [numeric(N)]
The decider class allocations.
b: [matrix(nrow = P_r, ncol = C)]
The matrix of class means as columns.
Omega: [matrix(nrow = P_r * P_r, ncol = C)]
The matrix of vectorized class covariance matrices as columns.
delta: [numeric(1)]
The prior concentration for s.
mu_b_0: [numeric(P_r)]
The mean vector of the normal prior for each b_c.
Sigma_b_0: [matrix(P_r, P_r)]
The covariance matrix of the normal prior for each b_c.
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.
identify_classes: [logical(1)]
Identify classes by decreasing class weights?
Cmax: [integer(1)]
The maximum number of classes, used to allocate space.

Value

A list of updated values for z, b, Omega, and C.

Examples

set.seed(1)
z <- c(rep(1, 10),rep(2, 10))
b <- matrix(c(5, 5, 5, -5), ncol = 2)
Omega <- matrix(c(1, 0.3, 0.3, 0.5, 1, -0.3, -0.3, 0.8), ncol = 2)
beta <- sapply(
  z, function(z) oeli::rmvnorm(n = 1, b[, z], matrix(Omega[, z], 2, 2))
)
beta[, 1] <- c(-10, 10)
update_classes_dp(
  beta = beta, z = z, b = b, Omega = Omega,
  delta = 1, mu_b_0 = numeric(2), Sigma_b_0 = diag(2),
  n_Omega_0 = 4, V_Omega_0 = diag(2)
)
#> $z
#>       [,1]
#>  [1,]    3
#>  [2,]    1
#>  [3,]    1
#>  [4,]    1
#>  [5,]    1
#>  [6,]    1
#>  [7,]    1
#>  [8,]    1
#>  [9,]    1
#> [10,]    1
#> [11,]    2
#> [12,]    2
#> [13,]    2
#> [14,]    2
#> [15,]    2
#> [16,]    2
#> [17,]    2
#> [18,]    2
#> [19,]    2
#> [20,]    2
#> 
#> $b
#>          [,1]      [,2]      [,3]
#> [1,] 4.271368  4.411489 -9.010132
#> [2,] 4.705080 -4.386000  6.669987
#> 
#> $Omega
#>          [,1]       [,2]       [,3]
#> [1,] 2.318979  1.3721770  0.7757253
#> [2,] 1.007656 -0.4716889 -1.9847570
#> [3,] 1.007656 -0.4716889 -1.9847570
#> [4,] 1.079144  0.5840789  7.4273879
#> 
#> $C
#> [1] 3
#>