First we load the `pcds`

package:

`library(pcds)`

Recall that a Delaunay cell is an interval in 1D space, a triangle in 2D space, and a tetrahedron in 3D space.
*Extreme points* or *extrema* are defined in a local or restricted sense, e.g.,
points closest to the center in a vertex or edge region
or closest to the opposite edge in a vertex region, etc of a Delaunay cell.
Such points are usually the best candidates for the (minimum) dominating sets for PCDs.

First we define the same triangle \(T\) used in the previous sections

```
<-c(1,1); B<-c(2,0); C<-c(1.5,2);
A<-rbind(A,B,C);
Tr
<-10 #try also n<-20 or 100 n
```

And we generate \(n=\) 10 uniform points in it
using the function `runif.tri`

.

```
set.seed(1)
<-runif.tri(n,Tr)$g Xp
```

Here an *extrema point* in each \(M\)-vertex region in a triangle \(T\), where \(M\) is a center,
is the point in the given data set, \(\mathcal{X}_n\) closest to the opposite edge.
That is, for the triangle \(T(A,B,C)\), if the \(M\)-vertex region for vertex \(A\) is \(V(A)\),
then the opposite edge is edge \(BC\), and closest point
in \(\mathcal X_n \cap V(A)\) to edge \(BC\) is found (if there are no data points in \(V(A)\),
the function returns `NA`

for the vertex region \(V(A)\)).
These extrema are used to find the minimum dominating set and hence the domination number of the PE-PCD,
since a subset (possibly all) of this type of extrema points constitutes a minimum dominating set
(see Ceyhan and Priebe (2007), Ceyhan and Priebe (2005), and Ceyhan (2011) for more details).

With `M=CC`

(i.e., when the center is the circumcenter),
the extrema point here is the closest \(\mathcal{X}\) point in CC-vertex
region to the opposite edge, and is found by the function `cl2edgesCC_VR`

which is an object of class `Extrema`

and has arguments `Xp,tri`

where

`Xp`

, a set of 2D points representing the set of data points and`tri`

, a \(3 \times 2\) matrix with each row representing a vertex of the triangle.

Its `call`

(with `Ext`

in the below script) just returns the type of the extrema.
Its `summary`

returns the type of the extrema, the extrema points,
distances between the edges and the closest points to the edges in CC-vertex regions,
vertices of the support of the data points.
The `plot`

function (or `plot.Extrema`

) would return
the plot of the triangle together with the CC-vertex regions and
the generated points with extrema being marked with red crosses.

```
<-cl2edgesCC_VR(Xp,Tr)
Ext
Ext#> Call:
#> cl2edgesCC_VR(Xp = Xp, tri = Tr)
#>
#> Type:
#> [1] "Closest Points in CC-Vertex Regions of the Triangle with Vertices A=(1,1), B=(2,0), and C=(1.5,2) \n to the Opoosite Edges"
summary(Ext)
#> Call:
#> cl2edgesCC_VR(Xp = Xp, tri = Tr)
#>
#> Type of the Extrema
#> [1] "Closest Points in CC-Vertex Regions of the Triangle with Vertices A=(1,1), B=(2,0), and C=(1.5,2) \n to the Opoosite Edges"
#>
#> Extremum Points: Closest Points in CC-Vertex Regions of the Triangle to its Edges
#> (Row i corresponds to vertex region i for i=1,2,3)
#> [,1] [,2]
#> [1,] 1.723711 0.8225489
#> [2,] 1.794240 0.2158873
#> [3,] 1.380035 1.5548904
#> [1] "Vertex labels are A=1, B=2, and C=3 (correspond to row number in Extremum Points)"
#>
#> Distances between the Edges and the Closest Points to the Edges in CC-Vertex Regions
#> (i-th entry corresponds to vertex region i for i=1,2,3)
#> [1] 0.06854235 1.06105561 0.66109225
#>
#> Vertices of the Support Triangle
#> [,1] [,2]
#> A 1.0 1
#> B 2.0 0
#> C 1.5 2
plot(Ext)
```

With `M=CM`

(i.e., when the center is the center of mass),
the extrema point here is the closest \(\mathcal{X}\) point in CM-vertex
region to the opposite edge, and is found by the function `cl2edgesCM_VR`

which is an object of class `Extrema`

.
Its arguments, `call`

, `summary`

and `plot`

are as in `cl2edgesCC_VR`

.

```
<-cl2edgesCM_VR(Xp,Tr)
Ext
Ext#> Call:
#> cl2edgesCM_VR(Xp = Xp, tri = Tr)
#>
#> Type:
#> [1] "Closest Points in CM-Vertex Regions of the Triangle with Vertices A=(1,1), B=(2,0), and C=(1.5,2) \n to the Opposite Edges"
summary(Ext)
#> Call:
#> cl2edgesCM_VR(Xp = Xp, tri = Tr)
#>
#> Type of the Extrema
#> [1] "Closest Points in CM-Vertex Regions of the Triangle with Vertices A=(1,1), B=(2,0), and C=(1.5,2) \n to the Opposite Edges"
#>
#> Extremum Points: Closest Points in CM-Vertex Regions of the Triangle to its Edges
#> (Row i corresponds to vertex region i for i=1,2,3)
#> [,1] [,2]
#> [1,] 1.316272 1.0372685
#> [2,] 1.687023 0.7682074
#> [3,] 1.482080 1.1991317
#> [1] "Vertex labels are A=1, B=2, and C=3 (correspond to row number in Extremum Points)"
#>
#> Distances between the Edges and the Closest Points to the Edges in CM-Vertex Regions
#> (i-th entry corresponds to vertex region i for i=1,2,3)
#> [1] 0.4117393 0.7181527 0.4816895
#>
#> Vertices of the Support Triangle
#> [,1] [,2]
#> A 1.0 1
#> B 2.0 0
#> C 1.5 2
plot(Ext)
```

With a general center `M`

in the interior of the triangle \(T\),
the extrema point here is the closest \(\mathcal{X}\) point in \(M\)-vertex
region to the opposite edge,
and is found by the function `cl2edgesM_VR`

which is an object of class `Extrema`

and has arguments `Xp,tri,M,alt`

where

`Xp,tri`

are as in`cl2edgesCC_VR`

.`M`

, a 2D point in Cartesian coordinates or a 3D point in barycentric coordinates which serves as a center in the interior of the triangle`tri`

or the circumcenter of`tri`

; which may be entered as “CC” as well;`alt`

, a logical argument for alternative method of finding the closest points to the edges, default`alt=FALSE`

. When`alt=FALSE`

, the function sequentially finds the vertex region of the data point and then the minimum distance to the opposite edge and the relevant extrema objects, and when`alt=TRUE`

, it first partitions the data set according which vertex regions they reside, and then finds the minimum distance to the opposite edge and the relevant extrema on each partition.

Its `call`

, `summary`

and `plot`

are as in `cl2edgesCC_VR`

.
We comment the `plot(Ext)`

out below for brevity in the exposition,
as the plot is similar to the one in `cl2edgesCM_VR`

above.

```
<-c(1.6,1.0) #try also M<-as.numeric(runif.tri(1,Tr)$g)
M<-cl2edgesM_VR(Xp,Tr,M)
Ext
Ext#> Call:
#> cl2edgesM_VR(Xp = Xp, tri = Tr, M = M)
#>
#> Type:
#> [1] "Closest Points in M-Vertex Regions of the Triangle with Vertices A=(1,1), B=(2,0), and C=(1.5,2)\n to Opposite Edges"
summary(Ext)
#> Call:
#> cl2edgesM_VR(Xp = Xp, tri = Tr, M = M)
#>
#> Type of the Extrema
#> [1] "Closest Points in M-Vertex Regions of the Triangle with Vertices A=(1,1), B=(2,0), and C=(1.5,2)\n to Opposite Edges"
#>
#> Extremum Points: Closest Points in M-Vertex Regions of the Triangle to its Edges
#> (Row i corresponds to vertex region i for i=1,2,3)
#> [,1] [,2]
#> [1,] 1.482080 1.1991317
#> [2,] 1.687023 0.7682074
#> [3,] 1.380035 1.5548904
#> [1] "Vertex labels are A=1, B=2, and C=3 (correspond to row number in Extremum Points)"
#>
#> Distances between the Edges and the Closest Points to the Edges in M-Vertex Regions
#> (i-th entry corresponds to vertex region i for i=1,2,3)
#> [1] 0.2116239 0.7181527 0.6610922
#>
#> Vertices of the Support Triangle
#> [,1] [,2]
#> A 1.0 1
#> B 2.0 0
#> C 1.5 2
plot(Ext)
```

There is also the function `cl2edgesVRbas.tri`

which takes arguments `Xp,c1,c2,M`

and finds the closest points in a data set `Xp`

in the \(M\)-vertex regions to the corresponding (opposite) edges in the standard basic triangle, \(T_b\)
where the *standard basic triangle* is \(T_b=T((0,0),(1,0),(c_1,c_2))\)
with \(0 < c_1 \le 1/2\), \(c_2>0\),
and \((1-c_1)^2+c_2^2 \le 1\).

Here an *extrema point* in each CC-vertex region
is the point in the given data set, \(\mathcal{X}_n\) closest to the circumcenter (CC).
That is, for the triangle \(T(A,B,C)\), if the \(CC\)-vertex region is \(V(A)\),
the closest point in \(\mathcal X_n \cap V(A)\) to the circumcenter is found
(if there are no data points in \(V(A)\), the function returns `NA`

for the vertex region \(V(A)\)).

The description and summary of extrema points and their plot can be obtained using the function `cl2CC.VR`

which is an object of class `Extrema`

and takes arguments `Xp,tri,ch.all.intri`

where

`Xp,tri`

are as in`cl2edgesCC_VR`

and`ch.all.intri`

is a logical argument (default=`FALSE`

) to check whether all data points are inside the triangle`tri`

. So, if it is`TRUE`

, the function checks if all data points are inside the closure of the triangle (i.e., interior and boundary combined) else it does not.

Its `call`

, `summary`

and `plot`

are as in `cl2edgesCC_VR`

, except in `summary`

distances between the circumcenter and the closest points to the circumcenter in CC-vertex regions are provided.

```
<-cl2CC.VR(Xp,Tr)
Ext
Ext#> Call:
#> cl2CC.VR(Xp = Xp, tri = Tr)
#>
#> Type:
#> [1] "Closest Points in CC-Vertex Regions of the Triangle with Vertices A=(1,1), B=(2,0), and C=(1.5,2) to its Circumcenter"
summary(Ext)
#> Call:
#> cl2CC.VR(Xp = Xp, tri = Tr)
#>
#> Type of the Extrema
#> [1] "Closest Points in CC-Vertex Regions of the Triangle with Vertices A=(1,1), B=(2,0), and C=(1.5,2) to its Circumcenter"
#>
#> Extremum Points: Closest Points in CC-Vertex Regions of the Triangle to its Circumcenter
#> (Row i corresponds to vertex i for i=1,2,3)
#> [,1] [,2]
#> [1,] 1.723711 0.8225489
#> [2,] 1.794240 0.2158873
#> [3,] 1.529720 1.5787125
#> [1] "Vertex labels are A=1, B=2, and C=3 (correspond to row number in Extremum Points)"
#>
#> Distances between the Circumcenter and the Closest Points to the Circumcenter in CC-Vertex Regions
#> (i-th entry corresponds to vertex i for i=1,2,3)
#> [1] 0.4442261 0.9143510 0.7428921
#>
#> Vertices of the Support Triangle
#> [,1] [,2]
#> A 1.0 1
#> B 2.0 0
#> C 1.5 2
plot(Ext)
```

There is also the function `cl2CC.VRbas.tri`

which takes arguments `Xp,c1,c2,ch.all.intri`

and
finds the closest points in a data set `Xp`

in the CC-vertex regions to the circumcenter in the standard basic triangle, \(T_b\).

Here an *extrema point* in each CC-vertex region
is the point in the given data set, \(\mathcal{X}_n\) furthest to the corresponding vertex.
That is, for the triangle \(T(A,B,C)\), if the \(CC\)-vertex region is \(V(A)\),
the furthest point in \(\mathcal X_n \cap V(A)\) from the vertex \(A\) is found
(if there are no data points in \(V(A)\), the function returns `NA`

for the vertex region \(V(A)\)).

The description and summary of extrema points and their plot can be obtained using the function `fr2vertsCC_VR`

which is an object of class `Extrema`

.
Its arguments, `call`

, `summary`

and `plot`

are as in `cl2edgesCC_VR`

,
except in `summary`

distances between the vertices and
the furthest points in the CC-vertex regions are provided.
We comment the `plot(Ext)`

out below for brevity in the exposition,
as the plot is a special case of the one in `kfr2vertsCC_VR`

with \(k=1\) below.

```
<-fr2vertsCC_VR(Xp,Tr)
Ext
Ext#> Call:
#> fr2vertsCC_VR(Xp = Xp, tri = Tr)
#>
#> Type:
#> [1] "Furthest Points in CC-Vertex Regions of the Triangle with Vertices A=(1,1), B=(2,0), and C=(1.5,2) from its Vertices"
summary(Ext)
#> Call:
#> fr2vertsCC_VR(Xp = Xp, tri = Tr)
#>
#> Type of the Extrema
#> [1] "Furthest Points in CC-Vertex Regions of the Triangle with Vertices A=(1,1), B=(2,0), and C=(1.5,2) from its Vertices"
#>
#> Extremum Points: Furthest Points in CC-Vertex Regions of the Triangle from its Vertices
#> (Row i corresponds to vertex i for i=1,2,3)
#> [,1] [,2]
#> [1,] 1.723711 0.8225489
#> [2,] 1.794240 0.2158873
#> [3,] 1.380035 1.5548904
#> [1] "Vertex labels are A=1, B=2, and C=3 (correspond to row number in Extremum Points)"
#>
#> Distances between the vertices and the furthest points in the vertex regions
#> (i-th entry corresponds to vertex i for i=1,2,3)
#> [1] 0.7451486 0.2982357 0.4609925
#>
#> Vertices of the Support Triangle
#> [,1] [,2]
#> A 1.0 1
#> B 2.0 0
#> C 1.5 2
plot(Ext)
```

There is also the function `fr2vertsCC_VRbas.tri`

which takes the same arguments as `cl2CC.VRbas.tri`

and
returns the furthest points in a data set in the vertex regions from the vertices in the standard basic triangle, \(T_b\).

Here *extrema points* in each CC-vertex region
are the \(k\) most furthest points in the given data set, \(\mathcal{X}_n\) to the corresponding vertex.
That is, for the triangle \(T(A,B,C)\), if the \(CC\)-vertex region is \(V(A)\),
the \(k\) most furthest points in \(\mathcal X_n \cap V(A)\) from the vertex \(A\) are found
(if there are \(k'\) with \(k'< k\) data points in \(V(A)\),
the function returns \(k-k'\) `NA`

’s for at the end of the extrema vector for the vertex region \(V(A)\)).

The description and summary of extrema points and their plot can be obtained using the function `kfr2vertsCC_VR`

.
which is an object of class `Extrema`

and takes arguments `Xp,tri,k,ch.all.intri`

where `Xp,tri,ch.all.intri`

are as in `fr2vertsCC_VR`

and `k`

represents the number of furthest points from each vertex.
Its `call`

, `summary`

and `plot`

are also as in `fr2vertsCC_VR`

,
except in `summary`

distances between the vertices and
the \(k\) most furthest points to the vertices in the CC-vertex regions are provided.

```
=3
k<-kfr2vertsCC_VR(Xp,Tr,k)
Ext
Ext#> Call:
#> kfr2vertsCC_VR(Xp = Xp, tri = Tr, k = k)
#>
#> Type:
#> [1] "3 Furthest Points in CC-Vertex Regions of the Triangle with Vertices A=(1,1), B=(2,0), and C=(1.5,2) from its Vertices"
summary(Ext)
#> Call:
#> kfr2vertsCC_VR(Xp = Xp, tri = Tr, k = k)
#>
#> Type of the Extrema
#> [1] "3 Furthest Points in CC-Vertex Regions of the Triangle with Vertices A=(1,1), B=(2,0), and C=(1.5,2) from its Vertices"
#>
#> Extremum Points: 3 Furthest Points in CC-Vertex Regions of the Triangle from its Vertices
#> (Row i corresponds to vertex i for i=1,2,3)
#> [,1] [,2]
#> 1. furthest from vertex 1 1.723711 0.8225489
#> 2. furthest from vertex 1 1.687023 0.7682074
#> 3. furthest from vertex 1 1.482080 1.1991317
#> 1. furthest from vertex 2 1.794240 0.2158873
#> 2. furthest from vertex 2 NA NA
#> 3. furthest from vertex 2 NA NA
#> 1. furthest from vertex 3 1.380035 1.5548904
#> 2. furthest from vertex 3 1.529720 1.5787125
#> 3. furthest from vertex 3 1.477620 1.7224190
#> [1] "Vertex labels are A=1, B=2, and C=3 (where vertex i corresponds to row numbers 3(i-1) to 3i in Extremum Points)"
#>
#> Distances between the vertices and the 3 furthest points in the vertex regions
#> (i-th entry corresponds to vertex i for i=1,2,3)
#> [,1] [,2] [,3]
#> [1,] 0.3686516 0.7250712 0.3511223
#> [2,] 0.2982357 NA NA
#> [3,] 0.4609925 0.4223345 0.2784818
#>
#> Vertices of the Support Triangle
#> [,1] [,2]
#> A 1.0 1
#> B 2.0 0
#> C 1.5 2
plot(Ext)
```

There is also the function `kfr2vertsCC_VRbas.tri`

which takes arguments `Xp,c1,c2,k,ch.all.intri`

and
finds the \(k\) most furthest points in a data set `Xp`

in the vertex regions from
the corresponding vertices in the standard basic triangle, \(T_b\).

Here *extrema points* are the points in the given data set, \(\mathcal{X}_n\) closest to the edges of the the standard equilateral triangle \(T_e=T(A,B,C)\) with vertices \(A=(0,0)\), \(B=(1,0)\), and \(C=(1/2,\sqrt{3}/2)\).
That is, for the triangle \(T_e\) the closest points
in \(\mathcal X_n \cap T_e\) to the edges of \(T_e\) are found.

First we generate \(n=20\) uniform points in the standard equilateral triangle \(T_e\).

```
<-10 #try also n<-20
n<-runif.std.tri(n)$gen.points Xp
```

The description and summary of extrema points and their plot can be obtained using the function `cl2edges.std.tri`

which is an object of class `Extrema`

with arguments `Xp,ch.all.intri`

which are as in `cl2CC.VR`

.
Its `call`

, `summary`

and `plot`

are as in `cl2edgesCC_VR`

,
except in `summary`

distances between the edges and the closest points to the edges in the standard equilateral triangle.
We used the option `asp=1`

in plotting so that \(T_e\) is actually depicted as an equilateral triangle.

```
<-cl2edges.std.tri(Xp)
Ext
Ext#> Call:
#> cl2edges.std.tri(Xp = Xp)
#>
#> Type:
#> [1] "Closest Points in the Standard Equilateral Triangle Te=T(A,B,C) with Vertices A=(0,0), B=(1,0), and C=(1/2,sqrt(3)/2) to its Edges"
summary(Ext)
#> Call:
#> cl2edges.std.tri(Xp = Xp)
#>
#> Type of the Extrema
#> [1] "Closest Points in the Standard Equilateral Triangle Te=T(A,B,C) with Vertices A=(0,0), B=(1,0), and C=(1/2,sqrt(3)/2) to its Edges"
#>
#> Extremum Points: Closest Points in the Standard Equilateral Triangle to its Edges
#> (Row i corresponds to edge i for i=1,2,3)
#> [,1] [,2]
#> [1,] 0.4763512 0.7726664
#> [2,] 0.4763512 0.7726664
#> [3,] 0.7111212 0.1053883
#> [1] "Edge labels are AB=3, BC=1, and AC=2 (correspond to row number in Extremum Points)"
#>
#> Distances between Edges and the Closest Points in the Standard Equilateral Triangle
#> (Row i corresponds to edge i for i=1,2,3)
#> [1] 0.06715991 0.02619907 0.10538830
#>
#> Vertices of the Support Standard Equilateral Triangle
#> [,1] [,2]
#> A 0.0 0.0000000
#> B 1.0 0.0000000
#> C 0.5 0.8660254
plot(Ext,asp=1)
```

Here an *extrema point* in each CM-edge region,
is the point in the given data set, \(\mathcal{X}_n\) furthest to the corresponding edge in the standard equilateral triangle.
That is, for the triangle \(T_e\), if the \(CM\)-edge region is \(E(AB)\),
the furthest point in \(\mathcal X_n \cap E(AB)\) from the edge \(AB\) is found
(if there are no data points in \(E(AB)\),
the function returns `NA`

for the edge region \(E(AB)\)).

The description and summary of extrema points and their plot can be obtained using the function `fr2edgesCM_ERstd.tri`

which is an object of class `Extrema`

which takes the same arguments as `cl2edges.std.tri`

.
Its `call`

, `summary`

and `plot`

are as in `cl2edgesCC_VR`

,
except in `summary`

distances between the edges and the furthest points in the CM-edge regions to the edges are provided.
We used the option `asp=1`

in plotting so that \(T_e\) is actually depicted as an equilateral triangle.

```
<-fr2edgesCM_ERstd.tri(Xp)
Ext
Ext#> Call:
#> fr2edgesCM_ERstd.tri(Xp = Xp)
#>
#> Type:
#> [1] "Furthest Points in the CM-Edge Regions of the Standard Equilateral Triangle T=(A,B,C) with A=(0,0), B=(1,0), and C=(1/2,sqrt(3)/2) from its Edges"
summary(Ext)
#> Call:
#> fr2edgesCM_ERstd.tri(Xp = Xp)
#>
#> Type of the Extrema
#> [1] "Furthest Points in the CM-Edge Regions of the Standard Equilateral Triangle T=(A,B,C) with A=(0,0), B=(1,0), and C=(1/2,sqrt(3)/2) from its Edges"
#>
#> Extremum Points: Furthest Points in the CM-Edge Regions of the Standard Equilateral Triangle from its Edges
#> (Row i corresponds to edge i for i=1,2,3)
#> [,1] [,2]
#> [1,] 0.6508705 0.2234491
#> [2,] 0.4590657 0.2878622
#> [3,] 0.7111212 0.1053883
#> [1] "Edge Labels are AB=3, BC=1, and AC=2 (correspond to row number in Extremum Points)"
#>
#> Distances between the edges and the furthest points in the edge regions
#> (i-th entry corresponds to edge i for i=1,2,3)
#> [1] 0.1906305 0.2536315 0.1053883
#>
#> Vertices of the Support Standard Equilateral Triangle
#> [,1] [,2]
#> A 0.0 0.0000000
#> B 1.0 0.0000000
#> C 0.5 0.8660254
plot(Ext,asp=1)
```

We use the same interval \((a,b)=(0,10)\) as in the previous sections.

```
<-.4
c<-0; b<-10; int<-c(a,b)
a
<-10
nx<-runif(nx,a,b) Xp
```

And we generate \(n_x=\) 10 uniform points in it by using the basic `R`

function `runif`

.

Here an *extrema point* in each \(M_c\)-vertex region, where \(M_c=a+c(b-a)\) for the interval \((a,b)\),
is the point in the given data set, \(\mathcal{X}_n\) closest to center \(M_c\).
For the interval \((a,b)\), if the \(M_c\)-vertex region is \(V(a)\),
the closest point in \(\mathcal X_n \cap V(a)\) to the center \(M_c\) is found.
That is, for the interval \((a,b)\),
the closest data point in \((M_c,b)\) to \(M_c\) and
closest data point in \((a,Mc)\) to \(M_c\) are found.
If there are no data points in \(V(A)\), the function returns `NA`

for the vertex region \(V(a)\).
These extrema are used in finding the minimum dominating set
and hence the domination number of the PE-PCDs in the 1D setting,
since a subset (possibly all) of these extrema constitutes a minimum dominating set
(see Priebe, DeVinney, and Marchette (2001), Ceyhan (2020), and Ceyhan (2008) for more details).

The description and summary of extrema points and their plot can be obtained using the function `cl2Mc.int`

which is an object of class `Extrema`

and takes arguments `Xp,int,c`

where

`Xp`

, a set or vector of 1D points from which closest points to \(M_c\) are found in the interval`int`

.`int`

, a vector of two real numbers representing an interval.`c`

, a positive real number in \((0,1)\) parameterizing the center inside`int`

\(=(a,b)\). For the interval,`int`

\(=(a,b)\), the parameterized center is \(M_c=a+c(b-a)\). . Its`call`

,`summary`

and`plot`

are as in`cl2edgesCC_VR`

, except in`summary`

distances between the center \(M_c\) and the closest points to \(M_c\) in \(M_c\)-vertex regions are provided.

```
<-cl2Mc.int(Xp,int,c)
Ext
Ext#> Call:
#> cl2Mc.int(Xp = Xp, int = int, c = c)
#>
#> Type:
#> [1] "Closest Points in Mc-Vertex Regions of the Interval (a,b) = (0,10) to its Center Mc = 4"
summary(Ext)
#> Call:
#> cl2Mc.int(Xp = Xp, int = int, c = c)
#>
#> Type of the Extrema
#> [1] "Closest Points in Mc-Vertex Regions of the Interval (a,b) = (0,10) to its Center Mc = 4"
#>
#> Extremum Points: Closest Points in Mc-Vertex Regions of the Interval to its Center
#> (i-th entry corresponds to vertex i for i=1,2)
#> [1] 2.454885 4.100841
#> [1] "Vertex Labels are a=1 and b=2 for the interval (a,b)"
#>
#> Distances between the Center Mc and the Closest Points to Mc in Mc-Vertex Regions
#> (i-th entry corresponds to vertex i for i=1,2)
#> [1] 1.5451149 0.1008408
#>
#> Vertices of the Support Interval
#> a b
#> 0 10
plot(Ext)
```

We illustrate the extrema for a tetrahedron which is obtained by slightly jittering the vertices of
the standard regular tetrahedron (for better visualization) and
generate \(n=20\) uniform \(\mathcal{X}\) points in it using the function `runif.tetra`

.

```
<-c(0,0,0); B<-c(1,0,0); C<-c(1/2,sqrt(3)/2,0); D<-c(1/2,sqrt(3)/6,sqrt(6)/3)
Aset.seed(1)
<-rbind(A,B,C,D)+matrix(runif(12,-.25,.25),ncol=3)
tetra<-10 #try also n<-20
n<-"CC" #try also "CM"
Cent
<-10 #try also n<-20
n<-runif.tetra(n,tetra)$g #try also Xp<-cbind(runif(n),runif(n),runif(n)) Xp
```

Here an *extrema point* in each \(M\)-vertex region, where \(M\) is a center of the tetrahedron,
is the point in the given data set, \(\mathcal{X}_n\) closest to the opposite face.
That is, for the tetrahedron \(T(A,B,C,D)\), if the \(M\)-vertex region is \(V(A)\),
the opposite face is the triangular face \(T(B,C,D)\), and closest point
in \(\mathcal X_n \cap V(A)\) to face \(T(B,C,D)\) is found
(if there are no data points in \(V(A)\), the function returns `NA`

for the vertex region \(V(A)\)).

With `M=CC`

(i.e., when the center is the circumcenter), the extrema point here is the closest \(\mathcal{X}\) point in CC-vertex
region to the opposite face, and is found by the function `cl2facesVRtetra`

which is an object of class `Extrema`

and takes arguments `Xp,th,M`

where

- `Xp1, a set of 3D points representing the set of data points.
- `th1, a \(4 \times 3\) matrix with each row representing a vertex of the tetrahedron.
`M1, the center to be used in the construction of the vertex regions in the tetrahedron,`

th`. Currently it only takes`

“CC”`for circumcenter and`

“CM”`for center of mass; default=`

“CM”`.

This function is the 3D version of the function `cl2edgesCC_VR`

Its `call`

, `summary`

and `plot`

are as in `cl2edgesCC_VR`

,
except in `summary`

distances between the faces and
the closest points to the faces in CC-vertex regions are provided.

```
<-cl2facesVRtetra(Xp,tetra,Cent)
Ext
Ext#> Call:
#> cl2facesVRtetra(Xp = Xp, th = tetra, M = Cent)
#>
#> Type:
#> [1] "Closest Points in CC-Vertex Regions of the Tetrahedron with Vertices A=(-0.12,-0.15,0.06), B=(0.94,0.2,-0.22), C=(0.54,1.09,-0.15), and D=(0.7,0.37,0.65) to the Opposite Faces"
summary(Ext)
#> Call:
#> cl2facesVRtetra(Xp = Xp, th = tetra, M = Cent)
#>
#> Type of the Extrema
#> [1] "Closest Points in CC-Vertex Regions of the Tetrahedron with Vertices A=(-0.12,-0.15,0.06), B=(0.94,0.2,-0.22), C=(0.54,1.09,-0.15), and D=(0.7,0.37,0.65) to the Opposite Faces"
#>
#> Extremum Points: Closest Points in CC-Vertex Regions of the Tetrahedron to its Faces
#> (Row i corresponds to face i for i=1,2,3,4)
#> [,1] [,2] [,3]
#> [1,] 0.1073281 0.0109421 0.19871179
#> [2,] 0.6535570 0.2922984 0.15795015
#> [3,] 0.5199352 0.6610763 0.08954581
#> [4,] 0.5127296 0.5449680 0.24057920
#> [1] "Vertex labels are A=1, B=2, C=3, and D=4 (correspond to row number in Extremum Points)"
#>
#> Distances between Faces and the Closest Points to the Faces in CC-Vertex Regions
#> (i-th entry corresponds to vertex region i for i=1,2,3,4)
#> [1] 0.7554212 0.2773495 0.4803672 0.3509001
#>
#> Vertices of the Support Tetrahedron
#> [,1] [,2] [,3]
#> A -0.1172457 -0.1491590 0.06455702
#> B 0.9360619 0.1991948 -0.21910686
#> C 0.5364267 1.0883630 -0.14701271
#> D 0.7041039 0.3690740 0.65477496
plot(Ext)
```

**References**

Ceyhan, E. 2008. “The Distribution of the Domination Number of Class Cover Catch Digraphs for Non-Uniform One-Dimensional Data.” *Discrete Mathematics* 308(23): 5376–93.

———. 2011. “Spatial Clustering Tests Based on Domination Number of a New Random Digraph Family.” *Communications in Statistics - Theory and Methods* 40(8): 1363–95.

———. 2020. “Domination Number of an Interval Catch Digraph Family and Its Use for Testing Uniformity.” *Statistics* 54(2): 310–39.

Ceyhan, E., and C. E. Priebe. 2005. “The Use of Domination Number of a Random Proximity Catch Digraph for Testing Spatial Patterns of Segregation and Association.” *Statistics & Probability Letters* 73(1): 37–50.

———. 2007. “On the Distribution of the Domination Number of a New Family of Parametrized Random Digraphs.” *Model Assisted Statistics and Applications* 1(4): 231–55.

Priebe, C. E., J. G. DeVinney, and D. J. Marchette. 2001. “On the Distribution of the Domination Number of Random Class Cover Catch Digraphs.” *Statistics & Probability Letters* 55(3): 239–46.