class_updates: Update latent classes
In RprobitB: Bayesian Probit Choice Modeling

class_updates

R Documentation

Update latent classes

Description

Low-level sampler kernels for class weights, allocations, sizes, the weight-based class update, and Dirichlet-process class updates.

Usage

sample_allocation(prob)

update_s(delta, m)

update_z(s, beta, b, Omega)

update_m(C, z, non_zero = FALSE)

update_classes_wb(
  s,
  b,
  Omega,
  epsmin = 0.01,
  epsmax = 0.7,
  deltamin = 0.1,
  deltashift = 0.5,
  identify_classes = FALSE,
  Cmax = 10L
)

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

`prob`	[`numeric(C)`] Class probabilities.
`delta`	[`numeric(1)`] Dirichlet concentration parameter.
`m`	[`numeric(C)`] Class sizes.
`s`	[`numeric(C)`] Class weights.
`beta`	[`matrix(P, N)`] Individual coefficient draws in columns.
`b`	[`matrix(P, C)`] Class means in columns.
`Omega`	[`matrix(P * P, C)`] Vectorized class covariance matrices in columns.
`C`	[`integer(1)`] Number of classes.
`z`	[`numeric(N)`] Class allocations numbered from one to `C`.
`non_zero`	[`logical(1)`] Replace empty class sizes by one?
`epsmin`	[`numeric(1)`] Remove the smallest class when its weight is below this threshold.
`epsmax`	[`numeric(1)`] Split the largest class when its weight exceeds this threshold.
`deltamin`	[`numeric(1)`] Merge the two closest classes when the Euclidean distance between their means is below this threshold.
`deltashift`	[`numeric(1)`] Scale of the mean displacement along the leading covariance eigenvector after splitting a class.
`identify_classes`	[`logical(1)`] Order the current active classes by size?
`Cmax`	[`integer(1)`] Maximum number of classes.
`mu_b_0`	[`numeric(P)`] Prior mean for class means.
`Sigma_b_0`	[`matrix(P, P)`] Prior covariance for class means.
`n_Omega_0`	[`integer(1)`] Prior degrees of freedom for class covariances.
`V_Omega_0`	[`matrix(P, P)`] Prior scale matrix for class covariances.

Value

The functions return one sampler update:

sample_allocation(): an integer class label.
update_s(): a C by 1 numeric matrix of class weights.
update_z(): an N by 1 numeric matrix of allocations.
update_m(): a C by 1 numeric matrix of class sizes.
update_classes_wb(): a list with s, b, Omega, and update_type, where the latter is zero for no update, one for removal, two for splitting, and three for merging.
update_classes_dp(): a list with z, b, Omega, and C.

References

\insertRef

Neal2000RprobitB

\insertRef

Oelschlaeger2021RprobitB

Examples

### a latent class state of six deciders with one random coefficient
set.seed(1)
beta <- matrix(c(-1, -1.2, -0.8, 1, 1.3, 0.9), nrow = 1)
b <- matrix(c(-1, 1), nrow = 1)
Omega <- matrix(c(0.2, 0.2), nrow = 1)

### the weights, the allocations, and the class sizes are drawn in turn
s <- update_s(delta = 1, m = c(3, 3))
z <- update_z(s, beta, b, Omega)
m <- update_m(C = 2, z = z)
sample_allocation(c(0.5, 0.3, 0.2))

### the weight-based update splits a class that grew too large
update_classes_wb(s = c(0.9, 0.1), b = b, Omega = Omega)

### the Dirichlet process update draws the class count from the data
update_classes_dp(
  beta = beta, z = z, b = b, Omega = Omega, delta = 1,
  mu_b_0 = 0, Sigma_b_0 = diag(1), n_Omega_0 = 4, V_Omega_0 = diag(1)
)

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