# itergfpop: Graph-constrained functional pruning optimal partitioning... In gfpop: Graph-Constrained Functional Pruning Optimal Partitioning

 itergfpop R Documentation

## Graph-constrained functional pruning optimal partitioning iterated

### Description

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 Birgé Massart like penalty

### Usage

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

### Arguments

 `data` vector of data to segment. For simulation studies, Data can be generated using gfpop package function `gfpop::dataGenerator()` `mygraph` dataframe of class "graph" to constrain the changepoint inference, see `gfpop::graph()` `type` a string defining the cost model to use: `"mean"`, `"variance"`, `"poisson"`, `"exp"`, `"negbin"` `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

### Value

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 Birgé Massart penalty until convergence

gfpop documentation built on March 18, 2022, 5:08 p.m.