Check prior parameters — check

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,
  mu_alpha_0 = numeric(P_f),
  Sigma_alpha_0 = 10 * diag(P_f),
  delta = 1,
  mu_b_0 = numeric(P_r),
  Sigma_b_0 = 10 * diag(P_r),
  n_Omega_0 = P_r + 2,
  V_Omega_0 = diag(P_r),
  n_Sigma_0 = J + 1,
  V_Sigma_0 = diag(J - 1),
  mu_d_0 = numeric(J - 2),
  Sigma_d_0 = diag(J - 2)
)

Arguments

P_f: [integer(1)]
The number of covariates connected to a fixed coefficient.
P_r: [integer(2)]
The number of covariates connected to a random coefficient.
J: [integer(1)]
The number >= 2 of choice alternatives.
ordered: [logical(1)]
If TRUE, the choice set alternatives is assumed to be ordered from worst to best.
mu_alpha_0: [numeric(P_f)]
The mean vector of the normal prior for alpha.
Sigma_alpha_0: [matrix(P_f, P_f)]
The covariance matrix of the normal prior for alpha.
delta: [numeric(1)]
The prior concentration for s.
mu_b_0: [numeric(P_r)]
The mean vector of the normal prior for each b_c.
Sigma_b_0: [matrix(P_r, P_r)]
The covariance matrix of the normal prior for each b_c.
n_Omega_0: [integer(1)]
The degrees of freedom of the Inverse Wishart prior for each Omega_c.
V_Omega_0: [matrix(P_r, P_r)]
The scale matrix of the Inverse Wishart prior for each Omega_c.
n_Sigma_0: [integer(1)]
The degrees of freedom of the Inverse Wishart prior for Sigma.
V_Sigma_0: [matrix(J - 1, J - 1)]
The scale matrix of the Inverse Wishart prior for Sigma.
mu_d_0: [numeric(J - 2)]
The mean vector of the normal prior for d .
Sigma_d_0: [matrix(J - 2, J - 2)]
The covariance matrix of the normal prior for d.

Value

An object of class RprobitB_prior, which is a list containing all prior parameters.

Details

A priori-distributions:

\(\alpha \sim N(\mu_{\alpha_0}, \Sigma_{\alpha_0})\)
\(s \sim Dir(\delta)\)
\(b_c \sim N(\mu_{b_0}, \Sigma_{b_0})\) for all \(c\)
\(\Omega_c \sim IW(n_{\Omega_0}, V_{\Omega_0})\) for all \(c\)
\(\Sigma \sim IW(n_{\Sigma_0}, V_{\Sigma_0})\)
\(d \sim N(\mu_{d_0}, \Sigma_{d_0})\)

Examples

check_prior(P_f = 1, P_r = 2, J = 3, ordered = TRUE)
#> $mu_alpha_0
#> [1] 0
#> 
#> $Sigma_alpha_0
#>      [,1]
#> [1,]   10
#> 
#> $delta
#> [1] 1
#> 
#> $mu_b_0
#> [1] 0 0
#> 
#> $Sigma_b_0
#>      [,1] [,2]
#> [1,]   10    0
#> [2,]    0   10
#> 
#> $n_Omega_0
#> [1] 4
#> 
#> $V_Omega_0
#>      [,1] [,2]
#> [1,]    1    0
#> [2,]    0    1
#> 
#> $n_Sigma_0
#> [1] NA
#> 
#> $V_Sigma_0
#> [1] NA
#> 
#> $mu_d_0
#> [1] 0
#> 
#> $Sigma_d_0
#>      [,1]
#> [1,]    1
#> 
#> attr(,"class")
#> [1] "RprobitB_prior" "list"