is_identifiable: Identifiability in case of latent variables

In gremes: Estimation of Tail Dependence in Graphical Models

Identifiability in case of latent variables

Description

It verifies if the identifiability criterion in case of latent variables is satisfied on the level of subsets. Basically it verifies for each subset whether the identifiability criterion is satisfied for the subgraph induced by this subset: verifies if every node with latent variables within the subgraph has degree at least three. It is applicable only for tree models.

Usage

is_identifiable(obj, tobj)

Arguments

obj	should be an object of class RootDepSet
tobj	should be an object of class Tree

Value

No return value

Examples

 seg<- graph(c(1,2,
2,3,
2,4,
4,5,
5,6,
5,7), directed = FALSE)
name_stat<- c("Paris", "2", "Meaux", "Melun", "5", "Nemours", "Sens")
seg<- set.vertex.attribute(seg, "name", V(seg), name_stat)
# we need some data to create the object of class "Tree"
seg_data<- matrix(rnorm(10*7), 10, 7)
colnames(seg_data)<- name_stat
tobj<- Tree(seg, seg_data[,c("Paris", "Meaux", "Melun", "Nemours", "Sens")])
# create the neighborhood of order one and call the function "is_identifiable"
nobj<- Neighborhood()
nobj<- subset(nobj, 1, seg, U_bar=getNoDataNodes(tobj))
is_identifiable(nobj, tobj)
nobj<- subset(nobj, 2, seg, U_bar=getNoDataNodes(tobj))
is_identifiable(nobj, tobj)
 # See also Vignette "Subsets and Coordinates"

gremes documentation built on Feb. 16, 2023, 8:06 p.m.