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


classification(x, add_true = FALSE)



An object of class RprobitB_fit.


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


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.


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.