check_prior: Check prior parameters

In RprobitB: Bayesian Probit Choice Modeling

Check prior parameters

Description

This function checks the compatibility of submitted parameters for the prior distributions and sets missing values to default values.

Usage

check_prior(
  P_f,
  P_r,
  J,
  ordered = FALSE,
  eta = numeric(P_f),
  Psi = diag(P_f),
  delta = 1,
  xi = numeric(P_r),
  D = diag(P_r),
  nu = P_r + 2,
  Theta = diag(P_r),
  kappa = if (ordered) 4 else (J + 1),
  E = if (ordered) diag(1) else diag(J - 1),
  zeta = numeric(J - 2),
  Z = diag(J - 2)
)

Arguments

P_f	The number of covariates connected to a fixed coefficient (can be 0).
P_r	The number of covariates connected to a random coefficient (can be 0).
J	The number (greater or equal 2) of choice alternatives.
ordered	A boolean, FALSE per default. If TRUE, the choice set alternatives is assumed to be ordered from worst to best.
eta	The mean vector of length P_f of the normal prior for alpha. Per default, eta = numeric(P_f).
Psi	The covariance matrix of dimension P_f x P_f of the normal prior for alpha. Per default, Psi = diag(P_f).
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).
kappa	The degrees of freedom (a natural number greater than J-1) of the Inverse Wishart prior for Sigma. Per default, kappa = J + 1.
E	The scale matrix of dimension J-1 x J-1 of the Inverse Wishart prior for Sigma. Per default, E = diag(J - 1).
zeta	The mean vector of length J - 2 of the normal prior for the logarithmic increments d of the utility thresholds in the ordered probit model. Per default, zeta = numeric(J - 2).
Z	The covariance matrix of dimension J-2 x J-2 of the normal prior for the logarithmic increments d of the utility thresholds in the ordered probit model. Per default, Z = diag(J - 2).

Details

A priori, we assume that the model parameters follow these distributions:

• \alpha \sim N(\eta, \Psi)

• s \sim Dir(\delta)

• b_c \sim N(\xi, D) for all classes c

• \Omega_c \sim IW(\nu,\Theta) for all classes c

• \Sigma \sim IW(\kappa,E)

• d \sim N(\zeta, Z)

where N denotes the normal, Dir the Dirichlet, and IW the Inverted Wishart distribution.

Value

An object of class RprobitB_prior, which is a list containing all prior parameters. Parameters that are not relevant for the model configuration are set to NA.

Examples

check_prior(P_f = 1, P_r = 2, J = 3, ordered = TRUE)

RprobitB documentation built on May 29, 2024, 7:59 a.m.