gibbs.sampler.posterior.rjags {ContRespPP}

Continuous Response Posterior Probability.

Description

gibbs.sampler.posterior.rjags obtains the ContRespPP method for obtaining the posterior (i.e., all observations have been seen, and this reverts to a traditional Bayesian analysis) using rjags to generate samples instead of base R functions, drastically decreasing the computational time required to obtain predictive draws.

Usage

gibbs.sampler.posterior.rjags(
  X,
  Y,
  beta.mean,
  beta.precision,
  shape,
  rate,
  b.sim,
  b.burnin,
  phi.0,
  prob,
  factor.no.2way = NA,
  colnames.pick = NA,
  seed = NA,
  verbose = TRUE
)

Arguments

X

Design matrix for the test (matrix of indicator functions defining which model parameters are active in each test event).

Y

A vector of the responses from the test.

beta.mean

Mean vector of the multivariate normal distribution (or the mean of each of the priors on the model parameters), ordered the same as the columns of the design matrix, X. It also serves as the initialization for the model parameters.

beta.precision

Precisions of the multivariate normal distribution (precision of each of the priors on the model parameters), corresponding to the beta.mean values.

shape

Hyperparameter alpha for gamma prior on the precision of the ANOVA model, tau.

rate

Hyperparameter beta for gamma prior on the precision of the ANOVA model, tau.

b.sim

Number of conditional posterior draws used in analysis for each non-conditional draw.

b.burnin

Number of burn-in samples for the conditional posterior.

phi.0

Threshold value the parameter of interest (BMM) must obtain (i.e., BBM > phi.0).

prob

Matrix or dataframe of the "likelihood of encountering" (or probability of seeing a factor level); it is a two column matrix (or dataframe), where the first column identifies the factor numerically and the second column defines the probability of seeing each factor level.

factor.no.2way

Optional vector of model parameters (as defined by prob) that are not incorporated in the two way interactions for the model.

colnames.pick

Optional vector of model parameter names in the same order as in the design matrix to label the returned dataframe columns.

seed

Optional selection which will create a reproducible result from the function.

verbose

Allows suppression of sampler progress printing in console.

Value

Returns a list with three elements:

pp

This value will be NA since this function only calculates the posterior

posterior

The full dataframe of non-conditional posterior draws

indicator

This value will be NA since this function only calculates the posterior

Printing the result object will display the predicted probability result.