itergfpop | R Documentation |

Functional pruning optimal partitioning with a graph structure to take into account constraints on consecutive segment parameters. This is an iterated version of the main gfpop function using a BirgĂ© Massart like penalty

```
itergfpop(
data,
mygraph,
type = "mean",
weights = NULL,
iter.max = 100,
D.init = 1
)
```

`data` |
vector of data to segment. For simulation studies, Data can be generated using gfpop package function |

`mygraph` |
dataframe of class "graph" to constrain the changepoint inference, see |

`type` |
a string defining the cost model to use: |

`weights` |
vector of weights (positive numbers), same size as data |

`iter.max` |
maximal number of iteration of the gfpop function |

`D.init` |
initialisation of the number of segments |

a gfpop object = (`changepoints, states, forced, parameters, globalCost`

)

`changepoints`

is the vector of changepoints (we give the last element of each segment)

`states`

is the vector giving the state of each segment

`forced`

is the vector specifying whether the constraints of the graph are active (

`= TRUE`

) or not (`= FALSE`

)`parameters`

is the vector of successive parameters of each segment

`globalCost`

is a number equal to the total loss: the minimal cost for the optimization problem with all penalty values excluded

`Dvect`

is a vector of integers. The successive tested D in the BirgĂ© Massart penalty until convergence

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