credible.region {bayesSurv} | R Documentation

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

### Description

See the two references for more detail.

### 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 arnost.komarek@med.kuleuven.be

### 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

### 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)
      }

[Package bayesSurv version 0.4 Index]