graph: Graph generation

In gfpop: Graph-Constrained Functional Pruning Optimal Partitioning

Graph generation

Description

Graph creation using component functions Edge, StartEnd and Node

Usage

graph(
  ...,
  type = "empty",
  decay = 1,
  gap = 0,
  penalty = 0,
  K = Inf,
  a = 0,
  all.null.edges = FALSE
)

Arguments

...	This is a list of edges definied by functions Edge, StartEnd and Node. See gfpop functions gfpop::Edge(), gfpop::StartEnd() and gfpop::Node()
type	a string equal to "std", "isotonic", "updown" or "relevant" to build a predefined classic graph
decay	a nonnegative number to give the strength of the exponential decay into the segment
gap	a nonnegative number to constrain the size of the gap in the change of state
penalty	a nonnegative number equals to the common penalty to use for all edges
K	a positive number. Threshold for the Biweight robust loss
a	a positive number. Slope for the Huber robust loss
all.null.edges	a boolean. Add null edges to all nodes automatically

Value

a dataframe with 9 variables : columns are named "state1", "state2", "type", "parameter", "penalty", "K", "a", "min", "max" with additional "graph" class.

Examples

graph(type = "updown", gap = 1.3, penalty = 5)

graph(Edge("Dw","Dw"),
      Edge("Up","Up"),
      Edge("Dw","Up","up", gap = 0.5, penalty = 10),
      Edge("Up","Dw","down"),
      StartEnd("Dw","Dw"),
      Node("Dw",0,1),
      Node("Up",0,1))

graph(Edge("1", "2", type = "std"),
      Edge("2", "3", type = "std"),
      Edge("3", "4", type = "std"),
      StartEnd(start = "1", end = "4"),
      all.null.edges = TRUE)

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