gibbs_sampling {RprobitB} | R Documentation |
Markov chain Monte Carlo simulation for the probit model
Description
This function draws from the posterior distribution of the probit model via
Markov chain Monte Carlo simulation-
Usage
gibbs_sampling(
sufficient_statistics,
prior,
latent_classes,
fixed_parameter,
init,
R,
B,
print_progress,
ordered,
ranked
)
Arguments
sufficient_statistics |
The output of sufficient_statistics .
|
prior |
A named list of parameters for the prior distributions. See the documentation
of check_prior for details about which parameters can be
specified.
|
latent_classes |
Either NULL (for no latent classes) or a list of parameters specifying
the number of latent classes and their updating scheme:
-
C : The fixed number (greater or equal 1) of latent classes,
which is set to 1 per default. If either weight_update = TRUE
or dp_update = TRUE (i.e. if classes are updated), C
equals the initial number of latent classes.
-
weight_update : A boolean, set to TRUE to weight-based
update the latent classes. See ... for details.
-
dp_update : A boolean, set to TRUE to update the latent
classes based on a Dirichlet process. See ... for details.
-
Cmax : The maximum number of latent classes.
-
buffer : The number of iterations to wait before a next
weight-based update of the latent classes.
-
epsmin : The threshold weight (between 0 and 1) for removing
a latent class in the weight-based updating scheme.
-
epsmax : The threshold weight (between 0 and 1) for splitting
a latent class in the weight-based updating scheme.
-
distmin : The (non-negative) threshold in class mean difference
for joining two latent classes in the weight-based updating scheme.
|
fixed_parameter |
Optionally specify a named list with fixed parameter values for alpha ,
C , s , b , Omega , Sigma , Sigma_full ,
beta , z , or d for the simulation.
See the vignette on model definition
for definitions of these variables.
|
init |
The output of set_initial_gibbs_values .
|
R |
The number of iterations of the Gibbs sampler.
|
B |
The length of the burn-in period, i.e. a non-negative number of samples to
be discarded.
|
print_progress |
A boolean, determining whether to print the Gibbs sampler progress and the
estimated remaining computation time.
|
ordered |
A boolean, FALSE per default. If TRUE , the choice set
alternatives is assumed to be ordered from worst to best.
|
ranked |
TBA
|
Details
This function is not supposed to be called directly, but rather via
fit_model
.
Value
A list of Gibbs samples for
-
Sigma
,
-
alpha
(if P_f>0
),
-
s
, z
, b
, Omega
(if P_r>0
),
-
d
(if ordered = TRUE
),
and a vector class_sequence
of length R
, where the r
th
entry is the number of latent classes after iteration r
.
[Package
RprobitB version 1.1.4
Index]