# Summarizing Bayesian change point analysis results

### Description

Summary and print methods for class `bcp`

.

### Usage

1 2 3 4 5 |

### Arguments

`object` |
the result of a call to |

`digits` |
the number of digits displayed in the summary statistics. |

`...` |
(optional) additional arguments, ignored. |

`x` |
the result of a call to |

### Details

The functions print (and return invisibly) the estimated posterior probability of a change point for each position and the estimated posterior means. These results are modeled after the summary method of the `coda`

package (Plummer *et al.*, 2006). If `return.mcmc=TRUE`

(i.e., if full MCMC results are returned), `bcp`

objects can be converted into `mcmc`

objects to view `mcmc`

summaries – see examples below.

### Value

The matrix of results is returned invisibly.

### Author(s)

Xiaofei Wang, Chandra Erdman, and John W. Emerson

### See Also

`bcp`

and `plot.bcp`

.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
##### A random sample from a few normal distributions #####
testdata <- c(rnorm(50), rnorm(50, 5, 1), rnorm(50))
bcp.0 <- bcp(testdata)
summary(bcp.0)
plot(bcp.0, main="Univariate Change Point Example")
##### An MCMC summary from the ``coda'' package #####
if (require("coda")) {
bcp.0 <- bcp(testdata, return.mcmc=TRUE)
bcp.mcmc <- as.mcmc(t(bcp.0$mcmc.means))
summary(bcp.mcmc)
heidel.diag(bcp.mcmc) # an example convergence diagnostic
# from the coda package.
}
``` |