# logLik.breakpoints

##### Log Likelihood and Information Criteria for Breakpoints

Computation of log likelihood and AIC type information criteria for partitions given by breakpoints.

- Keywords
- regression

##### Usage

```
# S3 method for breakpointsfull
logLik(object, breaks = NULL, ...)
# S3 method for breakpointsfull
AIC(object, breaks = NULL, ..., k = 2)
```

##### Arguments

- object
an object of class

`"breakpoints"`

or`"breakpointsfull"`

.- breaks
if

`object`

is of class`"breakpointsfull"`

the number of breaks can be specified.- …
*currently not used*.- k
the penalty parameter to be used, the default

`k = 2`

is the classical AIC,`k = log(n)`

gives the BIC, if`n`

is the number of observations.

##### Details

As for linear models the log likelihood is computed on a normal model and the degrees of freedom are the number of regression coefficients multiplied by the number of segments plus the number of estimated breakpoints plus 1 for the error variance.

If `AIC`

is applied to an object of class `"breakpointsfull"`

`breaks`

can be a vector of integers and the AIC for each corresponding
partition will be returned. By default the maximal number of breaks stored
in the `object`

is used. See below for an example.

##### Value

An object of class `"logLik"`

or a simple vector containing
the AIC respectively.

##### See Also

##### Examples

```
# NOT RUN {
## Nile data with one breakpoint: the annual flows drop in 1898
## because the first Ashwan dam was built
data("Nile")
plot(Nile)
bp.nile <- breakpoints(Nile ~ 1)
summary(bp.nile)
plot(bp.nile)
## BIC of partitions with0 to 5 breakpoints
plot(0:5, AIC(bp.nile, k = log(bp.nile$nobs)), type = "b")
## AIC
plot(0:5, AIC(bp.nile), type = "b")
## BIC, AIC, log likelihood of a single partition
bp.nile1 <- breakpoints(bp.nile, breaks = 1)
AIC(bp.nile1, k = log(bp.nile1$nobs))
AIC(bp.nile1)
logLik(bp.nile1)
# }
```

*Documentation reproduced from package strucchange, version 1.5-2, License: GPL-2 | GPL-3*