classification function

Classify deciders preference-based

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

classification(x, add_true = FALSE)

Arguments

• x: An object of class RprobitB_fit.
• add_true: Set to TRUE to add true class memberships to output (if available).

Returns

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

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.