itergfpop: Graph-constrained functional pruning optimal partitioning...

In gfpop: Graph-Constrained Functional Pruning Optimal Partitioning

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 April 1, 2023, 12:22 a.m.