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Set up a Naive Two-Stage Regression jags.model Object for a Given Prior

naive_jags_picker_2stage.Rd

Set up a Naive Two-Stage Regression jags.model Object for a Given Prior

Usage

naive_jags_picker_2stage(
  prior,
  sample_size,
  dim_x,
  dim_v,
  n_cat,
  Ystar,
  Ytilde,
  X,
  V,
  beta_prior_parameters,
  delta_prior_parameters,
  number_MCMC_chains,
  naive_model_file,
  display_progress = TRUE
)

Arguments

prior

character string specifying the prior distribution for the naive \(\beta\) parameters. Options are "t", "uniform", "normal", or "dexp" (double Exponential, or Weibull).

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 first-stage outcome mechanism, X.

dim_v

An integer specifying the number of columns of the design matrix of the second-stage outcome mechanism, V.

n_cat

An integer specifying the number of categorical values that the observed outcomes can take.

Ystar

A numeric vector of indicator variables (1, 2) for the first-stage observed outcome Y*. The reference category is 2.

Ytilde

A numeric vector of indicator variables (1, 2) for the second-stage observed outcome \(\tilde{Y}\). The reference category is 2.

X

A numeric design matrix for the true outcome mechanism.

V

A numeric design matrix for the second-stage outcome mechanism.

beta_prior_parameters

A numeric list of prior distribution parameters for the \(\beta\) terms. For prior distributions "t", "uniform", "normal", or "dexp", the first element of the list should contain a matrix of location, lower bound, mean, or shape parameters, respectively, for \(\beta\) terms. For prior distributions "t", "uniform", "normal", or "dexp", the second element of the list should contain a matrix of shape, upper bound, standard deviation, or scale parameters, respectively, for \(\beta\) terms. For prior distribution "t", the third element of the list should contain a matrix of the degrees of freedom for \(\beta\) terms. The third list element should be empty for all other prior distributions. All matrices in the list should have dimensions dim_x X n_cat, and all elements in the n_cat column should be set to NA.

delta_prior_parameters

A numeric list of prior distribution parameters for the naive \(\delta\) terms. For prior distributions "t", "uniform", "normal", or "dexp", the first element of the list should contain an array of location, lower bound, mean, or shape parameters, respectively, for \(\delta\) terms. For prior distributions "t", "uniform", "normal", or "dexp", the second element of the list should contain an array of shape, upper bound, standard deviation, or scale parameters, respectively, for \(\delta\) terms. For prior distribution "t", the third element of the list should contain an array of the degrees of freedom for \(\delta\) terms. The third list element should be empty for all other prior distributions. All arrays in the list should have dimensions n_cat X n_cat X dim_v, and all elements in the n_cat row should be set to NA.

number_MCMC_chains

An integer specifying the number of MCMC chains to compute.

naive_model_file

A .BUG file and used for MCMC estimation with rjags.

display_progress

A logical value specifying whether messages should be displayed during model compilation. The default is TRUE.

Value

naive_jags_picker_2stage returns a jags.model object for a naive two-stage regression model predicting the potentially misclassified Y* from the predictor matrix x and the potentially misclassified \(\tilde{Y} | Y^*\) from the predictor matrix v. The object includes the specified prior distribution, model, number of chains, and data.