classification: Classify deciders preference-based

In RprobitB: Bayesian Probit Choice Modeling

Classify deciders preference-based

Description

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

Usage

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).

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.

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.

See Also

update_z() for the updating function of the class allocation vector.

RprobitB documentation built on May 29, 2024, 7:59 a.m.