inci.mat.undPEstd.tri {pcds.ugraph} | R Documentation |
Incidence matrix for the underlying or reflexivity graph of Proportional Edge Proximity Catch Digraphs (PE-PCDs) - standard equilateral triangle case
Description
Returns the incidence matrix
for the underlying or reflexivity graph of the PE-PCD
whose vertices are the given 2D numerical data set, Xp
,
in the standard equilateral triangle
.
PE proximity region is constructed
with respect to the standard equilateral triangle with
expansion parameter
and vertex regions are based on
the center
in Cartesian coordinates
or
in barycentric coordinates
in the interior of
; default is
,
i.e., the center of mass of
.
Loops are allowed,
so the diagonal entries are all equal to 1.
See also (Ceyhan (2005, 2010)).
Usage
inci.mat.undPEstd.tri(
Xp,
r,
M = c(1, 1, 1),
ugraph = c("underlying", "reflexivity")
)
Arguments
Xp |
A set of 2D points which constitute the vertices of the underlying or reflexivity graph of the PE-PCD. |
r |
A positive real number
which serves as the expansion parameter in PE proximity region;
must be |
M |
A 2D point in Cartesian coordinates
or a 3D point in barycentric coordinates
which serves as a center
in the interior of the standard equilateral triangle |
ugraph |
The type of the graph based on PE-PCDs,
|
Value
Incidence matrix for the underlying or reflexivity graph
of the PE-PCD with vertices
being 2D data set, Xp
in the standard equilateral triangle where PE proximity
regions are defined with M
-vertex regions.
Author(s)
Elvan Ceyhan
References
Ceyhan E (2005).
An Investigation of Proximity Catch Digraphs in Delaunay Tessellations, also available as technical monograph titled Proximity Catch Digraphs: Auxiliary Tools, Properties, and Applications.
Ph.D. thesis, The Johns Hopkins University, Baltimore, MD, 21218.
Ceyhan E (2010).
“Extension of One-Dimensional Proximity Regions to Higher Dimensions.”
Computational Geometry: Theory and Applications, 43(9), 721-748.
Ceyhan E (2016).
“Edge Density of New Graph Types Based on a Random Digraph Family.”
Statistical Methodology, 33, 31-54.
See Also
inci.mat.undPEtri
, inci.mat.undPE
,
and inci.matPEstd.tri
Examples
#\donttest{
A<-c(0,0); B<-c(1,0); C<-c(1/2,sqrt(3)/2);
Te<-rbind(A,B,C)
n<-10
set.seed(1)
Xp<-pcds::runif.std.tri(n)$gen.points
M<-as.numeric(pcds::runif.std.tri(1)$g)
inc.mat<-inci.mat.undPEstd.tri(Xp,r=1.25,M)
inc.mat
(sum(inc.mat)-n)/2
num.edgesPEstd.tri(Xp,r=1.25,M)$num.edges
pcds::dom.num.greedy(inc.mat)
pcds::Idom.num.up.bnd(inc.mat,2)
#}