This function creates an object of class `RprobitB_parameter`

, which
contains the parameters of a probit model.
If `sample = TRUE`

, missing parameters are sampled. All parameters are
checked against the values of `P_f`

, `P_r`

, `J`

, and `N`

.

## Usage

```
RprobitB_parameter(
P_f,
P_r,
J,
N,
ordered = FALSE,
alpha = NULL,
C = NULL,
s = NULL,
b = NULL,
Omega = NULL,
Sigma = NULL,
Sigma_full = NULL,
beta = NULL,
z = NULL,
d = NULL,
seed = NULL,
sample = TRUE
)
```

## 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.

- N
The number (greater or equal 1) of decision makers.

- ordered
A boolean,

`FALSE`

per default. If`TRUE`

, the choice set`alternatives`

is assumed to be ordered from worst to best.- alpha
The fixed coefficient vector of length

`P_f`

. Set to`NA`

if`P_f = 0`

.- C
The number (greater or equal 1) of latent classes of decision makers. Set to

`NA`

if`P_r = 0`

. Otherwise,`C = 1`

per default.- s
The vector of class weights of length

`C`

. Set to`NA`

if`P_r = 0`

. For identifiability, the vector must be non-ascending.- b
The matrix of class means as columns of dimension

`P_r`

x`C`

. Set to`NA`

if`P_r = 0`

.- Omega
The matrix of class covariance matrices as columns of dimension

`P_r*P_r`

x`C`

. Set to`NA`

if`P_r = 0`

.- Sigma
The differenced error term covariance matrix of dimension

`J-1`

x`J-1`

with respect to alternative`J`

. In case of`ordered = TRUE`

, a numeric, the single error term variance.- Sigma_full
The error term covariance matrix of dimension

`J`

x`J`

. Internally,`Sigma_full`

gets differenced with respect to alternative`J`

, so it becomes an identified covariance matrix of dimension`J-1`

x`J-1`

.`Sigma_full`

is ignored if`Sigma`

is specified or`ordered = TRUE`

.- beta
The matrix of the decision-maker specific coefficient vectors of dimension

`P_r`

x`N`

. Set to`NA`

if`P_r = 0`

.- z
The vector of the allocation variables of length

`N`

. Set to`NA`

if`P_r = 0`

.- d
The numeric vector of the logarithmic increases of the utility thresholds in the ordered probit case (

`ordered = TRUE`

) of length`J-2`

.- seed
Set a seed for the sampling of missing parameters.

- sample
A boolean, if

`TRUE`

(default) missing parameters get sampled.

## Value

An object of class `RprobitB_parameter`

, i.e. a named list with the
model parameters `alpha`

, `C`

, `s`

, `b`

, `Omega`

,
`Sigma`

, `Sigma_full`

, `beta`

, and `z`

.

## Examples

```
RprobitB_parameter(P_f = 1, P_r = 2, J = 3, N = 10)
#> alpha : 0.8
#>
#> C : 1
#>
#> s : 1
#>
#> b : 2 x 1 matrix of doubles
#>
#> [,1]
#> [1,] -1.9
#> [2,] -2.1
#>
#>
#> Omega : 4 x 1 matrix of doubles
#>
#> [,1]
#> [1,] 3.483686
#> [2,] -2.746854
#> [3,] -2.746854
#> [4,] 3.874257
#>
#>
#> Sigma : 2 x 2 matrix of doubles
#>
#> [,1] [,2]
#> [1,] 3.1122967 0.4256672
#> [2,] 0.4256672 2.8436770
#>
#>
#> Sigma_full : 3 x 3 matrix of doubles
#>
#> [,1] [,2] [,3]
#> [1,] 6.6100780 0.8429134 3.1832096
#> [2,] 0.8429134 0.1803880 0.1026744
#> [3,] 3.1832096 0.1026744 2.8686378
#>
#>
#> beta : 2 x 10 matrix of doubles
#>
#> [,1] [,2] [,3] ... [,10]
#> [1,] -0.9191 -1.6917 -3.0293 ... -5.9343
#> [2,] -5.8519 -3.0123 -3.7019 ... 1.0129
#>
#>
#> z : integer vector of length 10
#>
#> 1 1 1 ... 1
#>
#> d : NA
#>
```