classification {RprobitB} | R Documentation |
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 |
add_true |
Set to |
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.