Log Likelihood and Information Criteria for Breakpoints

Description

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

Usage

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

Arguments

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 segements 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

breakpoints

Examples

if(! "package:stats" %in% search()) library(ts)

## 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)