# summary.bcp

From bcp v4.0.3
by Xiaofei Wang

##### Summarizing Bayesian change point analysis results

Summary and print methods for class `bcp`

.

- Keywords
- datasets

##### Usage

```
# S3 method for bcp
summary(object, digits = max(3, .Options$digits - 3), ...)
```# S3 method for bcp
print(x, digits = max(3, .Options$digits - 3), ...)

##### Arguments

- object
the result of a call to

`bcp()`

.- digits
the number of digits displayed in the summary statistics.

- ...
(optional) additional arguments, ignored.

- x
the result of a call to

`bcp()`

.

##### 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.

##### See Also

##### Examples

```
# NOT RUN {
##### 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 #####
# }
# NOT RUN {
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.
}
# }
```

*Documentation reproduced from package bcp, version 4.0.3, License: GPL (>= 2)*

### Community examples

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