## File: logLik.breakpoints.Rd

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 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768 \name{logLik.breakpoints} \alias{logLik.breakpoints} \alias{logLik.breakpointsfull} \alias{AIC.breakpointsfull} \title{Log Likelihood and Information Criteria for Breakpoints} \description{ Computation of log likelihood and AIC type information criteria for partitions given by breakpoints. } \usage{ \method{logLik}{breakpointsfull}(object, breaks = NULL, ...) \method{AIC}{breakpointsfull}(object, breaks = NULL, ..., k = 2) } \arguments{ \item{object}{an object of class \code{"breakpoints"} or \code{"breakpointsfull"}.} \item{breaks}{if \code{object} is of class \code{"breakpointsfull"} the number of breaks can be specified.} \item{\dots}{\emph{currently not used}.} \item{k}{the penalty parameter to be used, the default \code{k = 2} is the classical AIC, \code{k = log(n)} gives the BIC, if \code{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 \code{AIC} is applied to an object of class \code{"breakpointsfull"} \code{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 \code{object} is used. See below for an example. } \value{ An object of class \code{"logLik"} or a simple vector containing the AIC respectively. } \seealso{\code{\link{breakpoints}}} \examples{ ## 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) } \keyword{regression}