Log Likelihood and Information Criteria for Breakpoints
Computation of log likelihood and AIC type information criteria for partitions given by breakpoints.
## S3 method for class 'breakpointsfull' logLik(object, breaks = NULL, ...) ## S3 method for class 'breakpointsfull' AIC(object, breaks = NULL, ..., k = 2)
object |
an object of class |
breaks |
if |
... |
currently not used. |
k |
the penalty parameter to be used, the default |
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.
An object of class "logLik" or a simple vector containing
the AIC respectively.
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.