coefficient_updates: Update coefficient distributions
In RprobitB: Bayesian Probit Choice Modeling

coefficient_updates

R Documentation

Update coefficient distributions

Description

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))

### 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)
)
update_b(beta, Omega, z, m, Sigma_b_0_inv = diag(1), mu_b_0 = 0)

### 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)
)
update_Omega(
  beta, b = matrix(c(-1, 1), nrow = 1), z, m,
  n_Omega_0 = 4, V_Omega_0 = diag(1)
)

RprobitB documentation built on Sept. 20, 2026, 9:07 a.m.