Dirichlet process-based update of latent classes — update_classes

This function updates the latent classes based on a Dirichlet process.

Usage

update_classes_dp(
  Cmax,
  beta,
  z,
  b,
  Omega,
  delta,
  xi,
  D,
  nu,
  Theta,
  s_desc = TRUE
)

Arguments

Cmax: The maximum number of classes.
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.
delta: A numeric for the concentration parameter vector rep(delta,C) of the Dirichlet prior for s. Per default, delta = 1. In case of Dirichlet process-based updates of the latent classes, delta = 0.1 per default.
xi: The mean vector of length P_r of the normal prior for each b_c. Per default, xi = numeric(P_r).
D: The covariance matrix of dimension P_r x P_r of the normal prior for each b_c. Per default, D = diag(P_r).
nu: The degrees of freedom (a natural number greater than P_r) of the Inverse Wishart prior for each Omega_c. Per default, nu = P_r + 2.
Theta: The scale matrix of dimension P_r x P_r of the Inverse Wishart prior for each Omega_c. Per default, Theta = diag(P_r).
s_desc: If TRUE, sort the classes in descending class weight.

Value

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

Details

To be added.

Examples

set.seed(1)
z <- c(rep(1,20),rep(2,30))
b <- matrix(c(1,1,1,-1), 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) rmvnorm(b[,z], matrix(Omega[,z],2,2)))
delta <- 1
xi <- numeric(2)
D <- diag(2)
nu <- 4
Theta <- diag(2)
RprobitB:::update_classes_dp(
  Cmax = 10, beta = beta, z = z, b = b, Omega = Omega,
  delta = delta, xi = xi, D = D, nu = nu, Theta = Theta
)
#> $z
#>       [,1]
#>  [1,]    2
#>  [2,]    2
#>  [3,]    2
#>  [4,]    2
#>  [5,]    2
#>  [6,]    2
#>  [7,]    1
#>  [8,]    2
#>  [9,]    2
#> [10,]    2
#> [11,]    2
#> [12,]    1
#> [13,]    2
#> [14,]    2
#> [15,]    2
#> [16,]    2
#> [17,]    2
#> [18,]    3
#> [19,]    1
#> [20,]    2
#> [21,]    1
#> [22,]    1
#> [23,]    1
#> [24,]    1
#> [25,]    1
#> [26,]    1
#> [27,]    1
#> [28,]    1
#> [29,]    1
#> [30,]    1
#> [31,]    1
#> [32,]    1
#> [33,]    1
#> [34,]    3
#> [35,]    2
#> [36,]    1
#> [37,]    1
#> [38,]    1
#> [39,]    1
#> [40,]    1
#> [41,]    1
#> [42,]    1
#> [43,]    1
#> [44,]    1
#> [45,]    1
#> [46,]    1
#> [47,]    1
#> [48,]    1
#> [49,]    1
#> [50,]    3
#> 
#> $b
#>            [,1]     [,2]       [,3]
#> [1,]  1.1955613 1.381324 -0.4912123
#> [2,] -0.9791527 1.218404  0.4543349
#> 
#> $Omega
#>            [,1]      [,2]        [,3]
#> [1,]  0.7161232 0.4793177  0.37048920
#> [2,] -0.1869330 0.1085593 -0.03874837
#> [3,] -0.1869330 0.1085593 -0.03874837
#> [4,]  0.5616673 0.2745555  0.37381786
#> 
#> $s
#> [1] 0.60 0.34 0.06
#> 
#> $C
#> [1] 3
#>