jags_picker_2stage {COMBO}  R Documentation 
Set up a TwoStage Binary Outcome Misclassification jags.model
Object for a Given Prior
Description
Set up a TwoStage Binary Outcome Misclassification jags.model
Object for a Given Prior
Usage
jags_picker_2stage(
prior,
sample_size,
dim_x,
dim_z,
dim_v,
n_cat,
Ystar,
Ytilde,
X,
Z,
V,
beta_prior_parameters,
gamma_prior_parameters,
delta_prior_parameters,
number_MCMC_chains,
model_file,
display_progress = TRUE
)
Arguments
prior 
A character string specifying the prior distribution for the

sample_size 
An integer value specifying the number of observations in the sample. 
dim_x 
An integer specifying the number of columns of the design matrix of the true outcome mechanism, 
dim_z 
An integer specifying the number of columns of the design matrix of the firststage observation mechanism, 
dim_v 
An integer specifying the number of columns of the design matrix of the secondstage observation mechanism, 
n_cat 
An integer specifying the number of categorical values that the true outcome, 
Ystar 
A numeric vector of indicator variables (1, 2) for the firststage observed
outcome 
Ytilde 
A numeric vector of indicator variables (1, 2) for the secondstage observed
outcome 
X 
A numeric design matrix for the true outcome mechanism. 
Z 
A numeric design matrix for the firststage observation mechanism. 
V 
A numeric design matrix for the secondstage observation mechanism. 
beta_prior_parameters 
A numeric list of prior distribution parameters
for the 
gamma_prior_parameters 
A numeric list of prior distribution parameters
for the 
delta_prior_parameters 
A numeric list of prior distribution parameters
for the 
number_MCMC_chains 
An integer specifying the number of MCMC chains to compute. 
model_file 
A .BUG file and used
for MCMC estimation with 
display_progress 
A logical value specifying whether messages should be
displayed during model compilation. The default is 
Value
jags_picker
returns a jags.model
object for a twostage binary
outcome misclassification model. The object includes the specified
prior distribution, model, number of chains, and data.