credible.region: Compute a simultaneous credible region (rectangle) from a...

In factor.switching: Post-Processing MCMC Outputs of Bayesian Factor Analytic Models

Compute a simultaneous credible region (rectangle) from a sample for a vector valued parameter.

Description

See references below for more details. The function has been originally written for the archived bayesSurv package.

Usage

credible.region(sample, probs=c(0.90, 0.975))

Arguments

sample	a data frame or matrix with sampled values (one column = one parameter)
probs	probabilities for which the credible regions are to be computed

Value

A list (one component for each confidence region) of length equal to length(probs). Each component of the list is a matrix with two rows (lower and upper limit) and as many columns as the number of parameters giving the confidence region.

Author(s)

Arnost Komarek

References

Besag, J., Green, P., Higdon, D. and Mengersen, K. (1995). Bayesian computation and stochastic systems (with Discussion). Statistical Science, 10, 3 - 66, page 30

Held, L. (2004). Simultaneous inference in risk assessment; a Bayesian perspective In: COMPSTAT 2004, Proceedings in Computational Statistics (J. Antoch, Ed.), 213 - 222, page 214

Held, L. (2004b). Simultaneous posterior probability statements from Monte Carlo output. Journal of Computational and Graphical Statistics, 13, 20 - 35.

Examples

  m <- 10000
  sample <- data.frame(x1=rnorm(m), x2=rnorm(m), x3=rnorm(m))
  probs <- c(0.70, 0.90, 0.95)
  CR <- credible.region(sample, probs=probs)

  for (kk in 1:length(CR)){
    suma <- sum(sample$x1 >= CR[[kk]]["Lower", "x1"] & sample$x1 <= CR[[kk]]["Upper", "x1"] &
            sample$x2 >= CR[[kk]]["Lower", "x2"] & sample$x2 <= CR[[kk]]["Upper", "x2"] &
            sample$x3 >= CR[[kk]]["Lower", "x3"] & sample$x3 <= CR[[kk]]["Upper", "x3"])
    show <- c(suma/m, probs[kk])
    names(show) <- c("Empirical", "Desired")
    print(show)
  }

factor.switching documentation built on March 18, 2022, 6:49 p.m.