gibbs.sampler {ContRespPP}

Continuous Response Predictive Probability.

Description

gibbs.sampler runs the estimation of the continuous response ANOVA predictive probability.

Usage

gibbs.sampler(
  X,
  Y,
  n.seen,
  beta.mean,
  beta.precision,
  shape,
  rate,
  n.sim,
  y.burnin,
  b.sim,
  b.burnin,
  phi.0,
  theta.t,
  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.

n.seen

Number of test events already observed (i.e., the number of rows of the design matrix, X, that have been observed).

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.

n.sim

Number of non-conditional posterior draws (i.e., number of draws that will be returned to the user from the function after burn-in draws for the non-conditional draws are removed).

y.burnin

Number of burn-in samples for the non-conditional posterior.

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).

theta.t

Certainty threshold for the conditional posterior probability of the parameter of interest (the Bayesian mission mean, "BMM") obtaining phi.0 (i.e., BMM > phi.0) that the conditional posterior probability must obtain (the certainty threshold for conditional P(BMM > phi.0) ) must obtain for the question of interest to be evaluated as successfully passing the test.

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 incorposhaped 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.

Details

The ANOVA model includes main effects and two-way interactions. Priors on model parameters are assumed to be independent of each other; beta is then defined as the set of model parameters, which is multivariate normal.

Value

Returns a list with three elements:

pp

The predicted probability of the test ending in a successful evaluation of the question of interest

posterior

The full dataframe of non-conditional posterior draws

indicator

The vector of test success results for each posterior draw

Printing the result object will display the predicted probability result.