# logLik.breakpoints

0th

Percentile

##### 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.

breakpoints

##### Aliases
• logLik.breakpoints
• logLik.breakpointsfull
• AIC.breakpointsfull
##### 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

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