Description Usage Arguments Details Value References See Also Examples

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

1 2 3 4 5 6 7 8 |

`object` |
an object of class |

`breaks` |
if |

`...` |
for |

`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`

or `LWZ`

is applied to an object of class `"breakpointsfull"`

`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
in the `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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
## 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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