Description Usage Arguments Details Value Examples

The function applies user-specified stopping criteria to extract change-points from `object`

generated by `wbs`

or `sbs`

. For `object`

of class 'sbs', the function returns
change-points whose corresponding test statistic exceeds threshold given in `th`

. For `object`

of class 'wbs',
the change-points can be also detected using information criteria with penalties specified in `penalty`

.

1 2 3 4 5 6 7 8 9 | ```
changepoints(object, ...)
## S3 method for class 'sbs'
changepoints(object, th = NULL, th.const = 1.3, Kmax = NULL,
...)
## S3 method for class 'wbs'
changepoints(object, th = NULL, th.const = 1.3, Kmax = 50,
penalty = c("ssic.penalty", "bic.penalty", "mbic.penalty"), ...)
``` |

`object` |
an object of 'wbs' or 'sbs' class returned by, respectively, |

`...` |
further arguments that may be passed to the penalty functions |

`th` |
a vector of positive scalars |

`th.const` |
a vector of positive scalars |

`Kmax` |
a maximum number of change-points to be detected |

`penalty` |
a character vector with names of penalty functions used |

For the change-point detection based on thresholding (`object`

of class 'sbs' or 'wbs'), the user can either specify the thresholds in `th`

directly,
determine the maximum number `Kmax`

of change-points to be detected, or let `th`

depend on `th.const`

.

When `Kmax`

is given, the function automatically sets `th`

to the lowest threshold such that the number of detected change-points is lower or equal than `Kmax`

.
Note that for the BS algorithm it might be not possible to find the threshold such that exactly `Kmax`

change-points are found.

When `th`

and `Kmax`

are omitted, the threshold value is set to

*th=sigma * th.const* sqrt(2 log(n)),*

where sigma is the Median Absolute Deviation estimate of the noise level and *n* is the number of elements in `x`

.

For the change-point detection based on information criteria (`object`

of class 'wbs' only),
the user can specify both the maximum number of change-points (`Kmax`

) and a type of the penalty used.
Parameter `penalty`

should contain a list of characters with names of the functions of at least two arguments (`n`

and `cpt`

).
For each penalty given, the following information criterion is minimized over candidate sets of change-points `cpt`

:

*n/2 log(sigma_k)+ penalty(n,cpt),*

where *k* denotes the number of elements in *cpt*, *sigma_k* is the corresponding maximum
likelihood estimator of the residual variance.

`sigma` |
Median Absolute Deviation estimate of the noise level |

`th` |
a vector of thresholds |

`no.cpt.th` |
the number of change-points detected for each value of |

`cpt.th` |
a list with the change-points detected for each value of |

`Kmax` |
a maximum number of change-points detected |

`ic.curve` |
a list with values of the chosen information criteria |

`no.cpt.ic` |
the number of change-points detected for each information criterion considered |

`cpt.ic` |
a list with the change-points detected for each information criterion considered |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
#we generates gaussian noise + Poisson process signal with 10 jumps on average
set.seed(10)
N <- rpois(1,10)
true.cpt <- sample(1000,N)
m1 <- matrix(rep(1:1000,N),1000,N,byrow=FALSE)
m2 <- matrix(rep(true.cpt,1000),1000,N,byrow=TRUE)
x <- rnorm(1000) + apply(m1>=m2,1,sum)
# we apply the BS and WBS algorithms with default values for their parameters
s <- sbs(x)
w <- wbs(x)
s.cpt <- changepoints(s)
s.cpt
w.cpt <- changepoints(w)
w.cpt
#we can use different stopping criteria, invoking sbs/wbs functions is not necessary
s.cpt <- changepoints(s,th.const=c(1,1.3))
s.cpt
w.cpt <- changepoints(w,th.const=c(1,1.3))
w.cpt
``` |

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