Computation of log likelihood and AIC type information criteria for partitions given by breakpoints.
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an object of class
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.
LWZ is applied to an object of class
breaks can be a vector of integers and the AIC or LWZ for each corresponding
partition will be returned. By default the maximal number of breaks stored
object is used. See below for an example.
An object of class
"logLik" or a simple vector containing
the AIC respectively.
Liu, J., Wu, S., & Zidek, J. V. (1997). On segmented multivariate regression. Statistica Sinica, 497-525.
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## 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 with 0 to 5 breakpoints plot(0:5, AIC(bp.nile, k = log(bp.nile$nobs)), type = "b") ## AIC plot(0:5, AIC(bp.nile), type = "b") ## LWZ plot(0:5, LWZ(bp.nile), type = "b") ## BIC, AIC, LWZ, log likelihood of a single partition bp.nile1 <- breakpoints(bp.nile, breaks = 1) AIC(bp.nile1, k = log(bp.nile1$nobs)) AIC(bp.nile1) LWZ(bp.nile1) logLik(bp.nile1)
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