This function classifies the deciders based on their allocation to the components of the mixing distribution.

## Arguments

- x
An object of class

`RprobitB_fit`

.- add_true
Set to

`TRUE`

to add true class memberships to output (if available).

## Value

A data frame. The row names are the decider ids. The first `C`

columns
contain the relative frequencies with which the deciders are allocated to
the `C`

classes. Next, the column `est`

contains the estimated
class of the decider based on the highest allocation frequency. If
`add_true`

, the next column `true`

contains the true class
memberships.

## Details

The function can only be used if the model has at least one random effect
(i.e. `P_r >= 1`

) and at least two latent classes (i.e. `C >= 2`

).

In that case, let \(z_1,\dots,z_N\) denote the class allocations of the \(N\) deciders based on their estimated mixed coefficients \(\beta = (\beta_1,\dots,\beta_N)\). Independently for each decider \(n\), the conditional probability \(\Pr(z_n = c \mid s,\beta_n,b,\Omega)\) of having \(\beta_n\) allocated to class \(c\) for \(c=1,\dots,C\) depends on the class allocation vector \(s\), the class means \(b=(b_c)_c\) and the class covariance matrices \(Omega=(Omega_c)_c\) and is proportional to $$s_c \phi(\beta_n \mid b_c,Omega_c).$$

This function displays the relative frequencies of which each decider was allocated to the classes during the Gibbs sampling. Only the thinned samples after the burn-in period are considered.

## See also

`update_z()`

for the updating function of the class allocation vector.