check_prior function

Check prior parameters

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

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).

Returns

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.

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.

Examples

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