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Update coefficient distributions

Source: R/RcppExports.R
coefficient_updates.Rd

Low-level Gibbs sampler kernels for coefficient means and covariance matrices.

Usage

update_coefficient(mu_beta_0, Sigma_beta_0_inv, XSigX, XSigU)

update_b_c(bar_b_c, Omega_c, m_c, Sigma_b_0_inv, mu_b_0)

update_b(beta, Omega, z, m, Sigma_b_0_inv, mu_b_0)

update_Omega_c(S_c, m_c, n_Omega_0, V_Omega_0, correlated)

update_Omega(beta, b, z, m, n_Omega_0, V_Omega_0, correlated = NULL)

Arguments

mu_beta_0

[numeric(P)]
Prior mean for a coefficient vector.

Sigma_beta_0_inv

[matrix(P, P)]
Prior precision matrix for a coefficient vector.

XSigX

[matrix(P, P)]
Sum of design cross-products weighted by inverse error covariance.

XSigU

[numeric(P)]
Sum of design-utility products weighted by inverse error covariance.

bar_b_c

[numeric(P)]
Average individual coefficient vector in one class.

Omega_c

[matrix(P, P)]
Covariance matrix of one class.

m_c

[integer(1)]
Size of one class.

Sigma_b_0_inv

[matrix(P, P)]
Prior precision matrix for class means.

mu_b_0

[numeric(P)]
Prior mean for class means.

beta

[matrix(P, N)]
Individual coefficient draws in columns.

Omega

[matrix(P * P, C)]
Vectorized class covariance matrices in columns.

z

[numeric(N)]
Class allocations numbered from one to C.

m

[numeric(C)]
Class sizes.

S_c

[matrix(P, P)]
Scatter matrix for one class.

n_Omega_0

[integer(1)]
Prior degrees of freedom for class covariances.

V_Omega_0

[matrix(P, P)]
Prior scale matrix for class covariances.

correlated

[logical(P) | NULL]
Which random effects are correlated. Covariances between the other effects are zero. By default (NULL), all random effects are correlated.

b

[matrix(P, C)]
Class means in columns.

Value

The functions return one sampler update:

  • update_b_c(): a P by 1 numeric matrix containing a class mean.

  • update_b(): a P by C matrix of class means.

  • update_Omega_c(): a P by P class covariance matrix.

  • update_Omega(): a P * P by C matrix of vectorized covariances.

  • update_coefficient(): a P by 1 numeric coefficient matrix.

Examples

### four deciders with one random coefficient in two classes
set.seed(1)
beta <- matrix(c(-1, -1.2, 1, 1.3), nrow = 1)
Omega <- matrix(c(0.2, 0.2), nrow = 1)
z <- c(1, 1, 2, 2)
m <- c(2, 2)

### a coefficient from its conditional posterior
update_coefficient(c(0, 0), diag(2), diag(2), c(0, 0))
#>            [,1]
#> [1,] -0.4429697
#> [2,]  0.1298554

### the class means, for one class and for all classes at once
update_b_c(
  bar_b_c = c(0, 0), Omega_c = diag(2), m_c = 4,
  Sigma_b_0_inv = diag(2), mu_b_0 = c(0, 0)
)
#>            [,1]
#> [1,] -0.3737045
#> [2,]  0.7134313
update_b(beta, Omega, z, m, Sigma_b_0_inv = diag(1), mu_b_0 = 0)
#>            [,1]     [,2]
#> [1,] -0.9006497 0.798074

### the class covariances
update_Omega_c(
  S_c = diag(2), m_c = 4, n_Omega_0 = 4, V_Omega_0 = diag(2),
  correlated = c(TRUE, TRUE)
)
#>             [,1]        [,2]
#> [1,]  0.23204571 -0.04360997
#> [2,] -0.04360997  0.22649560
update_Omega(
  beta, b = matrix(c(-1, 1), nrow = 1), z, m,
  n_Omega_0 = 4, V_Omega_0 = diag(1)
)
#>           [,1]      [,2]
#> [1,] 0.2548452 0.3903286