inci.matCSstd.tri: Incidence matrix for Central Similarity Proximity Catch...

In elvanceyhan/pcds: Proximity Catch Digraphs and Their Applications

Incidence matrix for Central Similarity Proximity Catch Digraphs (CS-PCDs) - standard equilateral triangle case

Description

Returns the incidence matrix for the CS-PCD whose vertices are the given 2D numerical data set, Xp, in the standard equilateral triangle T_e=T(v=1,v=2,v=3)=T((0,0),(1,0),(1/2,\sqrt{3}/2)).

CS proximity region is defined with respect to the standard equilateral triangle T_e=T(v=1,v=2,v=3)=T((0,0),(1,0),(1/2,\sqrt{3}/2)) and edge regions are based on the center M=(m_1,m_2) in Cartesian coordinates or M=(\alpha,\beta,\gamma) in barycentric coordinates in the interior of T_e; default is M=(1,1,1) i.e., the center of mass of T_e. Loops are allowed, so the diagonal entries are all equal to 1.

See also (\insertCiteceyhan:Phd-thesis,ceyhan:arc-density-CS,ceyhan:test2014;textualpcds).

Usage

inci.matCSstd.tri(Xp, t, M = c(1, 1, 1))

Arguments

Xp	A set of 2D points which constitute the vertices of the CS-PCD.
t	A positive real number which serves as the expansion parameter in CS proximity region.
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 T_e; default is M=(1,1,1) i.e. the center of mass of T_e.

Value

Incidence matrix for the CS-PCD with vertices being 2D data set, Xp and CS proximity regions are defined in the standard equilateral triangle T_e with M-edge regions.

Author(s)

Elvan Ceyhan

References

\insertAllCited

See Also

inci.matCStri, inci.matCS and inci.matPEstd.tri

Examples

## Not run: 
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<-runif.std.tri(n)$gen.points

M<-as.numeric(runif.std.tri(1)$g)  #try also M<-c(.6,.2)

inc.mat<-inci.matCSstd.tri(Xp,t=1.25,M)
inc.mat
sum(inc.mat)-n
num.arcsCSstd.tri(Xp,t=1.25)

dom.num.greedy(inc.mat) #try also dom.num.exact(inc.mat)  #might take a long time for large n
Idom.num.up.bnd(inc.mat,1)

## End(Not run)

elvanceyhan/pcds documentation built on June 29, 2023, 8:12 a.m.