Nothing
VS2.1 - Illustration of PCDs in One Triangle"
In pcds: Proximity Catch Digraphs and Their Applications
knitr::opts_chunk$set(collapse = TRUE,comment = "#>",fig.width=6, fig.height=4, fig.align = "center")
\newcommand{\X}{\mathcal{X}}
\newcommand{\Y}{\mathcal{Y}}
First we load the pcds package:
library(pcds)
Functions for Construction and Simulation of PCDs and Related Quantities
For illustration of construction of PCDs and related quantities,
we first focus on one Delaunay cell,
because subdigraph of the PCDs with vertices in one Delaunay cell are disconnected from the remaining vertices.
Thus arc density and domination number of PCDs can be obtained by adding numbers of arcs
and domination numbers for the subdigraphs for Delaunay cells.
A Delaunay cell is an interval for 1D space,
a triangle for 2D space,
and a tetrahedron for 3D space.
The arc density
and the domination number of PE- and CS-PCDs for uniform data in two or higher dimensions,
is geometry invariant,
thus we may also focus on standard equilateral triangle in 2D space
and a standard regular tetrahedron in the 3D space.
Geometry invariance means that the distribution of arc density or domination number of these PCDs
does not depend on the form (lengths of edges and inner angles) of the triangle or (lengths of edges, areas of faces and inner angles) of the tetrahedron for uniform data.
So,
one can use the standard Delaunay cells for computations and also simulations to utilize symmetry
as the results extend to any type of Delaunay cell for uniform data.
For one dimensional uniform data,
the PCDs enjoy scale invariance, which means that the distribution of arc density and domination number
does not depend on the length of the interval support.
Thus, we present functions for a single Delaunay cell (and also for the standard Delaunay cells) in "Section VS2*" files.
Functions for PCDs with Vertices in One Triangle
Due to geometry invariance of PE- and CS-PCDs for uniform data (which will be the vertices of the PCDs)
in any triangle in 2D space,
most computations (and if needed data generation and simulations) can be done in the standard equilateral triangle,
and for AS-PCD, one can restrict attention to the standard basic triangle.
The standard equilateral triangle is $T_e=T(A,B,C)$ with vertices $A=(0,0)$, $B=(1,0)$, and $C=(1/2,\sqrt{3}/2)$
and the standard basic triangle is $T_b=T(A,B,C')$ with vertices $A=(0,0)$, $B=(1,0)$, and $C'=(c_1,c_2))$
with $0 < c_1 \le 1/2$, $c_2>0$,
and $(1-c_1)^2+c_2^2 \le 1$.
Most of the PCD functions we will illustrate in this section are counterparts of the functions
in Section "VS1_1_2DArtiData"
(i.e. one-interval counterparts of the functions for the multiple-triangle setting).
Sometimes we will be focusing on the standard equilateral triangle (for speed and ease of computation).
For more detail on the construction and appealing properties of PCDs in triangles,
see @ceyhan:comp-geo-2010 and @ceyhan:mcap2012.
We first choose an arbitrary triangle $T=T(A,B,C)$ with vertices $A=(1,1)$, $B=(2,0)$, and $C=(1.5,2)$,
which happens to be an obtuse triangle,
and choose an arbitrary point in the interior of this triangle as the center (to construct the vertex or edge regions).
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
Tr<-rbind(A,B,C);
n<-5
set.seed(1)
Xp<-runif.tri(n,Tr)$g #try also Xp<-cbind(runif(n,1,2),runif(n,0,2))
M<-c(1.6,1.2) #try also M<-as.numeric(runif.tri(1,Tr)$g) or M="CC"
Then we generate $n=$ r n $\X$ points inside the triangle $T$ using the function runif.tri in pcds.
$\X$ points are denoted as Xp and $\Y$ points in Section "VS1_1_2DArtiData"
correspond to the vertices of the triangle $T$.
Hence, if the argument Yp is used for a function for multiple triangles,
it is replaced with tri a $3 \times 2$ matrix in which rows represent the vertices of the triangle
for a function written for a general triangle,
with c1,c2 for a basic triangle,
or omitted for the standard equilateral triangle.
We plot the triangle $T$ and the $\X$ points in it using the below code,
and also add the vertex names using the text function from base R.
Xlim<-range(Tr[,1])
Ylim<-range(Tr[,2])
plot(Tr,pch=".",xlab="",ylab="",xlim=Xlim,ylim=Ylim+c(0,.1),main="Points in One Triangle")
polygon(Tr)
points(Xp)
#add the vertex names and annotation
txt<-rbind(Tr)
xc<-txt[,1]+c(-.01,.015,.02)
yc<-txt[,2]+c(.02,.02,.02)
txt.str<-c("A","B","C")
text(xc,yc,txt.str)
Alternatively, we can use the plotDelaunay.tri function in pcds to obtain the same plot by executing plotDelaunay.tri(Xp,Tr,xlab="x",ylab="y").
Functions for Arc-Slice PCDs with Vertices in One Triangle {#sec:AS-PCD-one-tri}
Arc-Slice (AS) proximity region for a point is the intersection of the triangle containing the point
and the circle centered the point with radius being
the minimum distance from the point to the vertices of the triangle.
So, we first check whether a point is inside a circle or not by using the function in.circle which returns TRUE
if the point is inside the circle, and FALSE otherwise.
This function takes arguments p,cent,rad,boundary
where
p is a 2D point to be checked whether it is inside the circle or not,cent a 2D point in Cartesian coordinates which serves as the center of the circle,rad a positive real number which serves as the radius of the circle,boundary a logical parameter (default=TRUE) to include boundary or not, so if it is TRUE,
the function checks if the point, p, lies in the closure of the circle (i.e., interior and
boundary combined); else, it checks if p lies in the interior of the circle.
cent<-c(1,1); rad<-1; p<-c(1.4,1.2) #try also cent<-runif(2); rad<-runif(1); p<-runif(2);
in.circle(p,cent,rad)
p<-c(.4,-.2)
in.circle(p,cent,rad)
#> [1] TRUE
#> [1] FALSE
The circle for an AS proximity region of an $\X$ point $x$ is centered at the point $x$
with radius being the distance from $x$ to the closest class $\Y$ point
(which is the closest vertex of the triangle $T$ in the one triangle setting).
The radius of a point from one class with respect to points from the other class is provided by the function radius
which takes arguments p,Y where
p is the point to find the radius for andY is the matrix of non-target points.
The function also returns the index and coordinates of the class $\Y$ point closest to the class $\X$ point.
This function works for points in other dimensions as well.
See ? radius for further description.
ny<-5
Y<-cbind(runif(ny),runif(ny))
A<-c(1,1);
radius(A,Y)
#> $rad
#> [1] 0.3767951
#>
#> $index.of.clYpnt
#> [1] 4
#>
#> $closest.Ypnt
#> [1] 0.6684667 0.8209463
The radii of points from one class with respect to points from the other class are found by using the function radii
which takes arguments x,y
where
x is a set of $d$-dimensional points for which the radii are computed. Radius of an x point equals to the
distance to the closest y point
andy is a set of $d$-dimensional points representing the reference points for the balls. That is, radius
of an x point is defined as the minimum distance to the y points.
In addition to the radii, the function also returns the indices and coordinates of the class $\Y$ points closest to the class $\X$ points.
This function works for points in other dimensions as well.
See ? radii for further description.
nx<-6
ny<-5
X<-cbind(runif(nx),runif(nx))
Y<-cbind(runif(ny),runif(ny))
Rad<-radii(X,Y)
Rad
#> $radiuses
#> [1] 0.38015546 0.23618472 0.02161322 0.48477828 0.05674010 0.16819521
#>
#> $indices.of.closest.points
#> [1] 4 4 4 4 1 3
#>
#> $closest.points
#> [,1] [,2]
#> [1,] 0.51863426 0.4590657
#> [2,] 0.51863426 0.4590657
#> [3,] 0.51863426 0.4590657
#> [4,] 0.51863426 0.4590657
#> [5,] 0.07067905 0.4068302
#> [6,] 0.31627171 0.2936034
The function NAStri is used for the construction of AS proximity regions taking the arguments
p the point for which the AS proximity region is to be found,tri the support triangle,M is the center to construct the vertex regions with defaultM="CC" (i.e. the circumcenter),rv the index of the vertex regions that the point p resides in with default rv=NULL
and dec a positive integer the number of decimals (default is 4) to round the
barycentric coordinates when checking whether the end points fall on the boundary of the triangle tri or not.
NAStri returns the proximity region as end points of the straight line segments on the boundary of the proximity region
(which also fall on the boundary of the triangle),
the end points of the arc slices which are the parts of the defining circle falling in the interior of the triangle
and the angles between the vectors joining P and the end points of the arc slices and the horizontal line crossing the point P.
It is also possible to specify the index of the vertex region for the point P with the argument rv=k for $k=1,2,3$,
where $k$ refers to the vertex in $k$-th row for the triangle Tr
(but this must be computed before such as in the code Rv<-rel.vert.triCC(P1,Tr)$rv; NAStri(P1,Tr,M,Rv)
and it must be compatible with
the vertex region for the point P).
P<-c(1.8,.5)
NAStri(P,Tr,M)
#> $L
#> [,1] [,2]
#> [1,] 2 0
#> [2,] 2 0
#>
#> $R
#> [,1] [,2]
#> [1,] 1.741176 1.035294
#> [2,] 1.300000 0.700000
#>
#> $arc.slices
#> [,1] [,2]
#> [1,] 1.741176 1.035294
#> [2,] 1.300000 0.700000
#>
#> $Angles
#> [1] 1.680247 2.761086
Indicator for the presence of an arc from a (data or $\X$) point to another for AS-PCDs is the function IarcAStri.
One can use it for points in the data set or for arbitrary points (as if they were in the data set).
It takes the arguments,
p1 a 2D point whose AS proximity region is constructed,p2 another 2D point. The function determines whether p2 is inside the AS proximity region of p1 or not,tri vertices of the support triangle, stacked row-wise, i.e., each row representing a vertex of the triangle,M the center of the triangle to construct the vertex regions with, default is M="CC" i.e., the circumcenter of tri
and rv the index of the M-vertex region in tri containing the point, either 1,2,3 or NULL (which is the default).
This function returns $I(p2 \in N_{AS}(p1))$, that is, returns 1 if p2 is in $N_{AS}(p1)$, 0 otherwise.
#between two arbitrary points P1 and P2
P1<-as.numeric(runif.tri(1,Tr)$g)
P2<-as.numeric(runif.tri(1,Tr)$g)
IarcAStri(P1,P2,Tr,M)
#> [1] 0
#between the first two points in Xp
IarcAStri(Xp[1,],Xp[2,],Tr,M)
#> [1] 0
AS proximity regions are defined with respect to the
vertices of the triangle (i.e., with respect to the vertex regions they reside in) and
vertex regions in each triangle are based on the center M
for circumcenter or $M=(\alpha,\beta,\gamma)$ in barycentric coordinates in the
interior of the triangle; default is M="CC" i.e., circumcenter of the triangle.
See @ceyhan:Phd-thesis, @ceyhan:comp-geo-2010, and @ceyhan:mcap2012 for more on AS-PCDs.
Number of arcs of the AS-PCD can be computed by the function num.arcsAStri.
The function num.arcsAStri is an object of class "NumArcs"
and takes Xp,tri,M="CC" as its arguments
where Xp is the data set, and the others are as above
and returns the list of
desc: A description of the PCD and the output num.arcs: Number of arcs of the AS-PCD,num.in.tri: Number of Xp points in the triangle tri,ind.in.tri: The vector of indices of the Xp points that reside in the triangle.tess.points: vertices of the support triangle (i.e. Yp points)vertices: vertices of the PCD (i.e. Xp points)
Narcs = num.arcsAStri(Xp,Tr) #with default M="CC"; try also num.arcsAStri(Xp,Tr,M)
summary(Narcs)
#> Call:
#> num.arcsAStri(Xp = Xp, tri = Tr)
#>
#> Description of the output:
#> Number of Arcs of the AS-PCD and the Related Quantities with vertices Xp in One Triangle
#>
#> Number of data (Xp) points in the triangle = 5
#> Number of arcs in the digraph = 10
#>
#> Indices of data points in the triangle:
#> 1 2 3 4 5
#>
#plot(Narcs)
The arc density of the AS-PCD can be computed by the function ASarc.dens.tri.
It takes the arguments Xp,tri,M="CC",tri.cor where Xp is the data set (or $\X$ points)
tri,M="CC" are as above.
Arc density can be adjusted (or corrected) for the points outside the triangle by using the option tri.cor=T,
default option is tri.cor=FALSE.
ASarc.dens.tri(Xp,Tr,M)
#> [1] 0.5
The incidence matrix of the AS-PCD for the one triangle case can be found by inci.matAStri,
using the inci.matAStri(Xp,Tr,M) command.
It takes the same arguments as the function num.arcsAStri.
Plot of the arcs in the digraph AS-PCD, which is based on the $M$-vertex regions with $M=($ r M[1],r M[2] $)$,
can be obtained by the function plotASarcs.tri, which is the one-tri counterpart of the function plotASarcs.
It takes arguments Xp,tri,M="CC" (as in num.arcsAStri) and other options for the plot function.
See the help page for the function using ? plotASarcs.tri.
Plot of the arcs of the above AS-PCD,
together with the vertex regions (with the option vert.reg = TRUE).
plotASarcs.tri(Xp,Tr,M,xlab="",ylab="",vert.reg = TRUE)
Or, one can use the default center M="CC", and can add vertex names and text to the figure (with vertex regions)
using the below code.
The part M = as.numeric(arcsAStri(Xp,Tr)$param) is optional,
for the below annotation of the plot since circumcenter is used for the vertex regions.
par(pty = "s")
plotASarcs.tri(Xp,Tr,asp=1,xlab="",ylab="",vert.reg = TRUE); M = (arcsAStri(Xp,Tr)$param)$c
CC<-circumcenter.tri(Tr)
#determine whether the center used for vertex regions is circumcenter or not
if (identical(M,CC) || identical(M,"CC"))
{cent<-CC
D1<-(B+C)/2; D2<-(A+C)/2; D3<-(A+B)/2;
Ds<-rbind(D1,D2,D3)
cent.name<-"CC"
} else
{cent<-M
cent.name<-"M"
Ds<-prj.cent2edges(Tr,M)
}
#add the vertex names and annotation
txt<-rbind(Tr,cent,Ds)
xc<-txt[,1]+c(-.02,.02,.02,.05,.05,-0.03,-.01)
yc<-txt[,2]+c(.02,.02,.02,.07,.02,.05,-.06)
txt.str<-c("A","B","C",cent.name,"D1","D2","D3")
text(xc,yc,txt.str)
Plot of the AS proximity regions can be obtained by the function plotASregs.tri.
It takes arguments Xp,tri,M="CC" (as in num.arcsAStri) and other options for the plot function.
See the help page for the function using ? plotASregs.tri.
M<-c(1.6,1.2) #try also M<-c(1.6620051,0.8136604) or M="CC"
par(pty = "s")
plotASregs.tri(Xp,Tr,M,vert.reg = T,xlab="",ylab="")
The function arcsAStri is an object of class "PCDs".
It takes arguments Xp,tri,M="CC" (as in ASarc.dens.tri).
The output list is as in the function arcsAS
(see Section "VS1_1_2DArtiData").
The plot function returns the same plot as in plotASarcs.tri with $M=($ r M[1],r M[2] $)$,
hence we comment it out below.
M=c(1.6,1.2) #try also M=c(1.6620051,0.8136604)
Arcs<-arcsAStri(Xp,Tr,M) #try also Arcs<-arcsAStri(Xp,Tr) #uses the default center, namely circumcenter for M
Arcs
#> Call:
#> arcsAStri(Xp = Xp, tri = Tr, M = M)
#>
#> Type:
#> [1] "Arc Slice Proximity Catch Digraph (AS-PCD) for 2D Points in the Triangle with Center M = (1.6,1.2)"
summary(Arcs)
#> Call:
#> arcsAStri(Xp = Xp, tri = Tr, M = M)
#>
#> Type of the digraph:
#> [1] "Arc Slice Proximity Catch Digraph (AS-PCD) for 2D Points in the Triangle with Center M = (1.6,1.2)"
#>
#> Vertices of the digraph = Xp
#> Partition points of the region = Tr
#>
#> Selected tail (or source) points of the arcs in the digraph
#> (first 6 or fewer are printed)
#> [,1] [,2]
#> [1,] 1.265509 0.7442478
#> [2,] 1.687023 0.7682074
#> [3,] 1.687023 0.7682074
#> [4,] 1.687023 0.7682074
#> [5,] 1.380035 1.5548904
#> [6,] 1.267221 0.7722282
#>
#> Selected head (or end) points of the arcs in the digraph
#> (first 6 or fewer are printed)
#> [,1] [,2]
#> [1,] 1.267221 0.7722282
#> [2,] 1.265509 0.7442478
#> [3,] 1.267221 0.7722282
#> [4,] 1.482080 1.1991317
#> [5,] 1.482080 1.1991317
#> [6,] 1.265509 0.7442478
#>
#> Parameters of the digraph
#> $center
#> [1] 1.6 1.2
#>
#> Various quantities of the digraph
#> number of vertices number of partition points
#> 5.0 3.0
#> number of triangles number of arcs
#> 1.0 10.0
#> arc density
#> 0.5
plot(Arcs)
To see the code and the plot for the arcs using the plot.PCDs function,
with vertex names and annotation added to the plot,
type ? arcsAStri.
Functions for Proportional Edge PCDs with Vertices in One Triangle {#sec:PE-PCD-one-tri}
In this section, we use the same triangle $T$ and generate data as in Section \@ref(sec:AS-PCD-one-tri).
#A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); n<-5
#set.seed(1); Xp<-runif.tri(n,Tr)$g
M<-c(1.6,1.0) #try also M<-as.numeric(runif.tri(1,Tr)$g)
r<-1.5 #try also r<-2
And choose the expansion parameter $r=$ r r to illustrate proportional edge (PE)
proximity regions and the associated PCDs.
The function NPEtri is used for the construction of PE proximity regions taking the arguments
p,tri,r,M=c(1,1,1),rv=NULL
where
p,tri,rv=NULL are as in the function NAStri,
andM is the center to construct the vertex regions with default M=c(1,1,1) (i.e. the center of mass),rv the index of the vertex regions that the point p resides in with default rv=NULL and r is the expansion parameter (must be $\ge 1$).
NPEtri returns the PE proximity region (i.e., the vertices of the triangular proximity region).
P<-c(1.8,.5)
NPEtri(P,Tr,r,M)
#> [,1] [,2]
#> [1,] 2.000 0.00
#> [2,] 1.550 0.45
#> [3,] 1.775 0.90
Indicator for the presence of an arc from a (data or $\X$) point to another for PE-PCDs is the function IarcPEtri.
One can use it for points in the data set or for arbitrary points (as if they were in the data set).
It takes the arguments, p1,p2,tri,r,M=c(1,1,1),rv=NULL where
-p1,p2,tri,rv=NULL are as in IarcAStri and
- r,M=c(1,1,1) are as in NPEtri.
This function returns $I(p2 \in N_{PE}(p1))$,
that is, returns 1 if p2 is in $N_{PE}(p1)$, 0 otherwise.
P1<-as.numeric(runif.tri(1,Tr)$g)
P2<-as.numeric(runif.tri(1,Tr)$g)
IarcPEtri(P1,P2,Tr,r,M)
#> [1] 1
IarcPEtri(Xp[1,],Xp[5,],Tr,r,M) #try also IarcPEtri(Xp[5,],Xp[1,],Tr,r,M)
#> [1] 0
Number of arcs of the PE-PCD can be computed by the function num.arcsPEtri,
which is an object of class "NumArcs" and takes arguments Xp,tri,r,M=c(1,1,1)
where Xp is the data set, and the others are as above.
The output is as in num.arcsAStri.
Narcs = num.arcsPEtri(Xp,Tr,r,M)
summary(Narcs)
#> Call:
#> num.arcsPEtri(Xp = Xp, tri = Tr, r = r, M = M)
#>
#> Description of the output:
#> Number of Arcs of the PE-PCD with vertices Xp and Quantities Related to the Support Triangle
#>
#> Number of data (Xp) points in the triangle = 5
#> Number of arcs in the digraph = 7
#>
#> Indices of data points in the triangle:
#> 1 2 3 4 5
#>
#plot(Narcs)
The arc density of the PE-PCD can be computed by the function PEarc.dens.tri.
It takes the arguments Xp,tri,r,M,tri.cor where Xp is the data set (or $\X$ points)
r,tri,M are as above
and returns output as list with elements
arc.dens, arc density of PE-PCD whose vertices are the 2D numerical data set, Xp,std.arc.dens, the arc density standardized by the mean and asymptotic variance of the arc
density of PE-PCD for uniform data in the triangle tri, andcaveat a warning as "The standardized arc density is only correct when M is the center of mass in the current version".
Arc density can be adjusted (or corrected) for the points outside the triangle by using the option tri.cor=TRUE as in the ASarc.dens.tri.
Moreover, the standardized arc density is only correct when $M$ is the center of mass in the current version.
PEarc.dens.tri(Xp,Tr,r,M)
#> $arc.dens
#> [1] 0.35
The incidence matrix of the PE-PCD for the one triangle case can be found by the function inci.matPEtri, using e.g. the inci.matPEtri(Xp,Tr,r,M) command.
It takes the same arguments as the function num.arcsPEtri.
Plot of the arcs in the digraph PE-PCD, which is based on the $M$-vertex regions with $M=$(r M[1],r M[2]),
can be obtained by the function plotPEarcs.tri, which is the one-tri counterpart of the function plotPEarcs.
It takes arguments Xp,tri,r,M=c(1,1,1) (as in num.arcsPEtri) and other options for the plot function.
See the help page for the function using ? plotPEarcs.tri.
Plot of the arcs of the above PE-PCD, together with the vertex regions
(with the option vert.reg = TRUE)
and vertex names added to the figure.
plotPEarcs.tri(Xp,Tr,r,M,xlab="",ylab="",vert.reg = TRUE)
#add vertex labels and text to the figure (with vertex regions)
ifelse(isTRUE(all.equal(M,circumcenter.tri(Tr))),
{Ds<-rbind((B+C)/2,(A+C)/2,(A+B)/2); cent.name="CC"},{Ds<-prj.cent2edges(Tr,M); cent.name="M"})
txt<-rbind(Tr,M,Ds)
xc<-txt[,1]+c(-.02,.02,.02,.02,.04,-0.03,-.01)
yc<-txt[,2]+c(.02,.02,.02,.05,.02,.04,-.06)
txt.str<-c("A","B","C",cent.name,"D1","D2","D3")
text(xc,yc,txt.str)
Plots of the PE proximity regions can be obtained by the function plotPEregs.tri.
It takes arguments Xp,tri,r,M=c(1,1,1) (as in num.arcsPEtri) and other options for the plot function.
See the help page for the function using ? plotPEregs.tri.
M<-c(1.6,1.2) #try also M<-c(1.6620051,0.8136604) or M="CC"
plotPEregs.tri(Xp,Tr,r,M,vert.reg = T,xlab="",ylab="")
The function ArcsPEtri is an object of class "PCDs".
It takes arguments Xp,tri,r,M=c(1,1,1) (as in num.arcsPEtri).
The output list is as in the arcsAStri
except the parameters
of the digraph (center for PE-PCD and the expansion parameter).
The plot function returns the same plot as in plotPEarcs.tri,
hence we comment it out below.
Arcs<-ArcsPEtri(Xp,Tr,r,M) #or try with the default center Arcs<-ArcsPEtri(Xp,Tr,r); M= (Arcs$param)$cent
Arcs
#> Call:
#> ArcsPEtri(Xp = Xp, tri = Tr, r = r, M = M)
#>
#> Type:
#> [1] "Proportional Edge Proximity Catch Digraph (PE-PCD) for 2D Points in the Triangle with Expansion Parameter r = 1.5 and Center M = (1.6,1.2)"
summary(Arcs)
#> Call:
#> ArcsPEtri(Xp = Xp, tri = Tr, r = r, M = M)
#>
#> Type of the digraph:
#> [1] "Proportional Edge Proximity Catch Digraph (PE-PCD) for 2D Points in the Triangle with Expansion Parameter r = 1.5 and Center M = (1.6,1.2)"
#>
#> Vertices of the digraph = Xp
#> Partition points of the region = Tr
#>
#> Selected tail (or source) points of the arcs in the digraph
#> (first 6 or fewer are printed)
#> [,1] [,2]
#> [1,] 1.265509 0.7442478
#> [2,] 1.380035 1.5548904
#> [3,] 1.380035 1.5548904
#> [4,] 1.380035 1.5548904
#> [5,] 1.380035 1.5548904
#> [6,] 1.267221 0.7722282
#>
#> Selected head (or end) points of the arcs in the digraph
#> (first 6 or fewer are printed)
#> [,1] [,2]
#> [1,] 1.267221 0.7722282
#> [2,] 1.265509 0.7442478
#> [3,] 1.687023 0.7682074
#> [4,] 1.267221 0.7722282
#> [5,] 1.482080 1.1991317
#> [6,] 1.265509 0.7442478
#>
#> Parameters of the digraph
#> $center
#> [1] 1.6 1.2
#>
#> $`expansion parameter`
#> [1] 1.5
#>
#> Various quantities of the digraph
#> number of vertices number of partition points
#> 5.0 3.0
#> number of triangles number of arcs
#> 1.0 10.0
#> arc density
#> 0.5
plot(Arcs)
To see the code and the plot for the arcs using the plot.PCDs function,
with vertex names and annotation added to the plot,
type ? ArcsPEtri.
Functions for Central Similarity PCDs with Vertices in One Triangle {#sec:CS-PCD-one-tri}
We use the same triangle $T$ and generated data as Section \@ref(sec:PE-PCD-one-tri).
tau<-1.5
And choose the expansion parameter $\tau=$ r tau
to illustrate central similarity proximity regions and the associated PCDs.
The function NCStri is used for the construction of CS proximity regions taking the arguments
p,tri,t,M=c(1,1,1),re=NULL where
p,tri,M=c(1,1,1) are as in the function NPEtri,
andre the index of the edge regions that the point p resides in with default re=NULL and t is the expansion parameter (must be $> 0$).
NCStri returns the CS proximity region (i.e., the vertices of the triangular proximity region).
P<-c(1.8,.5)
NCStri(P,Tr,tau,M)
#> [,1] [,2]
#> [1,] 1.74375 0.9500
#> [2,] 1.51250 0.4875
#> [3,] 1.97500 0.0250
Indicator for the presence of an arc from a (data or $\X$) point to another for CS-PCDs is the function IarcCStri.
One can use it for points in the data set or for arbitrary points (as if they were in the data set).
It takes the arguments, p1,p2,tri,t,M,re=NULL where p1,p2,tri,M are as in IarcPEtri
and t,re=NULL are as in NCStri.
This function returns $I(p2 \in N_{CS}(p1))$, that is, returns 1 if p2 is in $N_{CS}(p1)$, 0 otherwise.
P1<-as.numeric(runif.tri(1,Tr)$g)
P2<-as.numeric(runif.tri(1,Tr)$g)
IarcCStri(P1,P2,Tr,tau,M)
#> [1] 1
IarcCStri(Xp[1,],Xp[2,],Tr,tau,M)
#> [1] 0
Number of arcs of the CS-PCD can be computed by the function num.arcsCStri.
The output is as in num.arcsPEtri.
Number of arcs of the CS-PCD can be computed by the function num.arcsCStri,
which is an object of class "NumArcs" and takes arguments Xp,tri,t,M=c(1,1,1)
where Xp is the data set, and the others are as above.
The output is as in num.arcsPEtri.
Narcs = num.arcsCStri(Xp,Tr,t=.5,M)
summary(Narcs)
#> Call:
#> num.arcsCStri(Xp = Xp, tri = Tr, t = 0.5, M = M)
#>
#> Description of the output:
#> Number of Arcs of the CS-PCD with vertices Xp and Quantities Related to the Support Triangle
#>
#> Number of data (Xp) points in the triangle = 5
#> Number of arcs in the digraph = 0
#>
#> Indices of data points in the triangle:
#> 1 2 3 4 5
#>
#plot(Narcs)
The arc density of the CS-PCD can be computed by the function CSarc.dens.tri.
It takes the arguments Xp,tri,t,M=c(1,1,1),tri.cor=FALSE where Xp is the data set (or $\X$ points)
tri,t,M=c(1,1,1),tri.cor=FALSE are as above.
Arc density can be adjusted (or corrected) for the points outside the triangle by using the option tri.cor=TRUE as in the num.arcsPEtri.
CSarc.dens.tri(Xp,Tr,tau,M)
#> $arc.dens
#> [1] 0.35
The incidence matrix of the CS-PCD for the one triangle case can be found by inci.matCStri, using the inci.matCStri(Xp,Tr,tau,M) command.
It takes the same arguments as the function num.arcsCStri.
Plot of the arcs in the digraph CS-PCD, which is based on the $M$-edge regions with $M=$(r M[1],r M[2]).
can be obtained by the function plotCSarcs.tri, which is the one-tri counterpart of the function plotCSarcs.
It takes arguments Xp,tri,t,M=c(1,1,1) (as in num.arcsPEtri) and other options for the plot function.
See the help page for the function using ? plotCSarcs.tri.
Plot of the arcs of the above CS-PCD, together with the edge regions
(with the option edge.reg = TRUE)
and vertex names added to the figure.
t<-1.5 #try also t<-2
plotCSarcs.tri(Xp,Tr,t,M,xlab="",ylab="",edge.reg = TRUE)
txt<-rbind(Tr,M)
xc<-txt[,1]+c(-.02,.02,.02,.03)
yc<-txt[,2]+c(.02,.02,.02,.03)
txt.str<-c("A","B","C","M")
text(xc,yc,txt.str)
Plot of the CS proximity regions can be obtained by the function plotCSregs.tri,
the first is for all points, the second is for two $\X$ points only (for better visualization).
It takes arguments Xp,tri,t,M=c(1,1,1) (as in num.arcsCStri) and other options for the plot function.
See the help page for the function using ? plotCSregs.tri.
plotCSregs.tri(Xp,Tr,t,M,edge.reg=T,xlab="",ylab="")
The function arcsCStri is an object of class "PCDs".
It takes arguments Xp,tri,t,M=c(1,1,1) (as in num.arcsCStri).
The output list is as in the ArcsPEtri.
The plot function returns the same plot as in plotCSarcs.tri,
hence we comment it out below.
Arcs<-arcsCStri(Xp,Tr,t,M)
Arcs
#> Call:
#> arcsCStri(Xp = Xp, tri = Tr, t = t, M = M)
#>
#> Type:
#> [1] "Central Similarity Proximity Catch Digraph (CS-PCD) for 2D Points in the Triangle with Expansion Parameter t = 1.5 and Center M = (1.6,1.2)"
summary(Arcs)
#> Call:
#> arcsCStri(Xp = Xp, tri = Tr, t = t, M = M)
#>
#> Type of the digraph:
#> [1] "Central Similarity Proximity Catch Digraph (CS-PCD) for 2D Points in the Triangle with Expansion Parameter t = 1.5 and Center M = (1.6,1.2)"
#>
#> Vertices of the digraph = Xp
#> Partition points of the region = Tr
#>
#> Selected tail (or source) points of the arcs in the digraph
#> (first 6 or fewer are printed)
#> [,1] [,2]
#> [1,] 1.687023 0.7682074
#> [2,] 1.687023 0.7682074
#> [3,] 1.687023 0.7682074
#> [4,] 1.267221 0.7722282
#> [5,] 1.482080 1.1991317
#> [6,] 1.482080 1.1991317
#>
#> Selected head (or end) points of the arcs in the digraph
#> (first 6 or fewer are printed)
#> [,1] [,2]
#> [1,] 1.265509 0.7442478
#> [2,] 1.267221 0.7722282
#> [3,] 1.482080 1.1991317
#> [4,] 1.265509 0.7442478
#> [5,] 1.265509 0.7442478
#> [6,] 1.687023 0.7682074
#>
#> Parameters of the digraph
#> $center
#> [1] 1.6 1.2
#>
#> $`expansion parameter`
#> [1] 1.5
#>
#> Various quantities of the digraph
#> number of vertices number of partition points
#> 5.0 3.0
#> number of triangles number of arcs
#> 1.0 8.0
#> arc density
#> 0.4
plot(Arcs)
To see the code and the plot for the arcs using the plot.PCDs function,
with vertex names and annotation added to the plot,
type ? arcsCStri.
Functions for Proportional Edge PCDs with Vertices in the Standard Equilateral Triangle {#sec:PE-PCD-inTe}
We first define the standard equilateral triangle $T_e=T(A,B,C)$ with vertices $A=(0,0)$, $B=(1,0)$, and $C=(1/2,\sqrt{3}/2)$.
A<-c(0,0); B<-c(1,0); C<-c(1/2,sqrt(3)/2);
Te<-rbind(A,B,C);
n<-5 #try also n<-10, 50, or 100
set.seed(1)
Xp<-runif.std.tri(n)$gen.points
M<-c(.6,.2) #try also M<-c(1,1,1)
Then we generate data or $\X$ points of size $n=$ r n using the function runif.std.tri in the pcds package,
and choose the arbitrary center $M=($ r M[1],r M[2] $)$ in the interior of $T_e$.
Notice that the argument tri is redundant for the functions specific to the standard equilateral triangle,
hence they are omitted for the functions which have counterparts in Section \@ref(sec:PE-PCD-one-tri).
Plot of the triangle $T_e$ and the $\X$ points in it can be obtained by the below code,
and we also add the vertex names and
annotation using the text function from base R.
We use asp=1 in the below plot so that the standard equilateral triangle is plotted with equal length edges.
Xlim<-range(Te[,1])
Ylim<-range(Te[,2])
plot(Te,asp=1,pch=".",xlab="",ylab="",xlim=Xlim,ylim=Ylim,main="Points in Standard Equilateral Triangle")
polygon(Te)
points(Xp)
#add the vertex names and annotation
txt<-rbind(Te)
xc<-txt[,1]+c(-.02,.02,.02)
yc<-txt[,2]+c(.01,.01,.01)
txt.str<-c("A","B","C")
text(xc,yc,txt.str)
Alternatively, we can use the plotDelaunay.tri function in pcds to obtain the same plot by executing plotDelaunay.tri(Xp,Te,xlab="x",ylab="y",main="Points in Standard Equilateral Triangle") command.
Indicator for the presence of an arc from a (data or $\X$) point to another for PE-PCDs is the function IarcPEstd.tri.
One can use it for points in the data set or for arbitrary points (as if they were in the data set).
It takes the arguments, p1,p2,r,M=c(1,1,1),rv=NULL as in IarcPEtri.
P1<-as.numeric(runif.tri(1,Te)$g)
P2<-as.numeric(runif.tri(1,Te)$g)
r=2
IarcPEstd.tri(P1,P2,r,M)
#> [1] 1
IarcPEstd.tri(Xp[1,],Xp[2,],r,M)
#> [1] 1
Number of arcs of the PE-PCD can be computed by the function num.arcsPEstd.triwhich
which is an object of class "NumArcs" and takes arguments as the function num.arcsPEtri.
The output is as in num.arcsPEtri.
Narcs = num.arcsPEstd.tri(Xp,r=1.25,M)
summary(Narcs)
#> Call:
#> num.arcsPEstd.tri(Xp = Xp, r = 1.25, M = M)
#>
#> Description of the output:
#> Number of Arcs of the PE-PCD and the Related Quantities with vertices Xp in the Standard Equilateral Triangle
#>
#> Number of data (Xp) points in the triangle = 5
#> Number of arcs in the digraph = 5
#>
#> Indices of data points in the triangle:
#> 1 2 3 4 5
#>
#plot(Narcs)
The incidence matrix of the PE-PCD for the one triangle case can be found by inci.matPETe,
using the inci.matPETe(Xp,r,M) command.
Functions for Central Similarity PCDs with Vertices in the Standard Equilateral Triangle
We use the same setting for the data points and the center as in Section \@ref(sec:PE-PCD-inTe).
Notice that the argument tri is redundant for the functions specific to the standard equilateral triangle,
hence they are omitted for the functions which have counterparts in Section \@ref(sec:CS-PCD-one-tri).
Indicator for the presence of an arc from a (data or $\X$) point to another for CS-PCDs is the function IarcPEstd.tri.
P1<-as.numeric(runif.tri(1,Te)$g)
P2<-as.numeric(runif.tri(1,Te)$g)
tau=1
IarcCSstd.tri(P1,P1,tau,M)
IarcCSstd.tri(P1,P2,tau,M)
IarcCSstd.tri(Xp[1,],Xp[2,],tau,M)
Number of arcs of the CS-PCD can be computed by the function num.arcsCSstd.tri
which is an object of class "NumArcs" and its
arguments and output are as in num.arcsCStri.
set.seed(123)
M<-as.numeric(runif.std.tri(1)$g) #try also M<-c(.6,.2)
Narcs = num.arcsCStri(Xp,Te,t=1.5,M)
summary(Narcs)
#> Call:
#> num.arcsCStri(Xp = Xp, tri = Te, t = 1.5, M = M)
#>
#> Description of the output:
#> Number of Arcs of the CS-PCD with vertices Xp and Quantities Related to the Support Triangle
#>
#> Number of data (Xp) points in the triangle = 5
#> Number of arcs in the digraph = 3
#>
#> Indices of data points in the triangle:
#> 1 2 3 4 5
#>
#plot(Narcs)
The incidence matrix of the CS-PCD for the one triangle case can be found by inci.matCSstd.tri,
using e.g. the inci.matCSstd.tri(Xp,t=1.5,M) command.
Auxiliary Functions to Define Proximity Regions for Points in a Triangle
The PCDs are constructed using proximity regions, and proximity region of an $\X$ point
depends on its location relative to the $\Y$ points.
In particular, AS- and PE-PCDs depend on the vertex regions
and CS-PCDs depend on edge regions.
We will illustrate the vertex and edge regions in this section.
These regions partition the Delaunay cell (e.g., Delaunay triangle in $\mathbb R^2$)
based on a center or central point in the triangle.
We first check whether a point is inside a triangle or not which can be done using the function in.triangle,
which takes arguments p,tri,boundary=FALSE where
p, a 2D point to be checked whether it is inside the triangle or not,tri A $3 \times 2$ matrix with each row representing a vertex of the triangle,boundary, a logical parameter (default=TRUE) to include boundary or not, so if it is TRUE,
the function checks if the point, p, lies in the closure of the triangle (i.e., interior and boundary
combined); else, it checks if p lies in the interior of the triangle.
The function gives TRUE if the point is inside the triangle, and FALSE otherwise.
It also returns the barycentric coordinates of the point with respect to the triangle.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); p<-c(1.4,1.2)
Tr<-rbind(A,B,C)
in.triangle(p,Tr)
#> $in.tri
#> [1] TRUE
#>
#> $barycentric
#> [1] 0.4 0.2 0.4
We now illustrate circumcenter of a triangle.
The function circumcenter.tri takes tri as its sole argument for a triangle and
returns the circumcenter of the triangle as its output.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
Tr<-rbind(A,B,C); #the vertices of the triangle Tr
(CC<-circumcenter.tri(Tr)) #the circumcenter
#> [1] 2.083333 1.083333
We plot the circumcenter of an obtuse triangle below (so circumcenter is outside of the triangle)
using the below code,
see also the help page with ? circumcenter.tri for the code to generate this figure.
Notice that in the code we used asp=1 so that lines joining CC to the edges of the triangle
appear perpendicular to the edges.
D1<-(B+C)/2; D2<-(A+C)/2; D3<-(A+B)/2; #midpoints of the edges
Ds<-rbind(D1,D2,D3)
Xlim<-range(Tr[,1],CC[1])
Ylim<-range(Tr[,2],CC[2])
xd<-Xlim[2]-Xlim[1]
yd<-Ylim[2]-Ylim[1]
par(pty="s")
plot(A,asp=1,pch=".",xlab="",ylab="",
main="Circumcenter of a Triangle",
axes=TRUE,xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05))
polygon(Tr)
points(rbind(CC))
L<-matrix(rep(CC,3),ncol=2,byrow=TRUE); R<-Ds
segments(L[,1], L[,2], R[,1], R[,2], lty=2)
txt<-rbind(Tr,CC,Ds)
xc<-txt[,1]+c(-.08,.08,.08,.12,-.09,-.1,-.09)
yc<-txt[,2]+c(.02,-.02,.03,-.06,.02,.06,-.04)
txt.str<-c("A","B","C","CC","D1","D2","D3")
text(xc,yc,txt.str)
The function circumcenter.basic.tri is a special case of circumcenter.tri and takes the argument c1,c2
and returns the circumcenter of the standard basic triangle.
The function center.nondegPE takes arguments tri,r
which are the triangle and the expansion parameter for the PE proximity regions, respectively.
It returns the three centers for non-degenerate asymptotic distribution of
domination number of PE-PCDs for $r \in (1,1.5)$ and the center of mass for $r=1.5$.
This center is not defined for $r > 1.5$, as the asymptotic distribution is degenerate in this case.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
Tr<-rbind(A,B,C);
r<-1.35
(Ms<-center.nondegPE(Tr,r))
#> [,1] [,2]
#> M1 1.388889 1.0000000
#> M2 1.611111 0.7777778
#> M3 1.500000 1.2222222
We plot the non-degeneracy centers for the triangle Tr using the below code,
type also ? center.nondegPE for the code to generate this figure.
Xlim<-range(Tr[,1])
Ylim<-range(Tr[,2])
xd<-Xlim[2]-Xlim[1]
yd<-Ylim[2]-Ylim[1]
plot(Tr,pch=".",xlab="",ylab="",
main="Centers of nondegeneracy of the domination number\n of the PE-PCD in a triangle",
axes=TRUE,xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05))
polygon(Tr)
points(Ms,pch=".",col=1)
polygon(Ms,lty=2)
xc<-Tr[,1]+c(-.02,.02,.02)
yc<-Tr[,2]+c(.02,.02,.03)
txt.str<-c("A","B","C")
text(xc,yc,txt.str)
xc<-Ms[,1]+c(-.04,.04,.03)
yc<-Ms[,2]+c(.02,.02,.05)
txt.str<-c(expression(M[1]),"M2","M3")
text(xc,yc,txt.str)
The function prj.cent2edges returns the projections of a point (e.g., a center) $M$ inside a triangle to its edges,
i.e., it returns the intersection point where the line joining a vertex to $M$ crosses the opposite edge.
The line segments between $M$ the intersection points define the $M$-vertex regions.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
Tr<-rbind(A,B,C);
M<-c(1.6,1.0) #try also M<-as.numeric(runif.tri(1,Tr)$g)
(Ds<-prj.cent2edges(Tr,M)) #try also prj.cent2edges(Tr,M=c(1,1))
#> [,1] [,2]
#> [1,] 1.750000 1.0000000
#> [2,] 1.333333 1.6666667
#> [3,] 1.666667 0.3333333
We plot the projections of center $M$ to the edges of the triangle Tr using the below code,
type also ? prj.cent2edges for the code to generate this figure.
M<-c(1.6,1.0)
Xlim<-range(Tr[,1])
Ylim<-range(Tr[,2])
xd<-Xlim[2]-Xlim[1]
yd<-Ylim[2]-Ylim[1]
if (dimension(M)==3) {M<-bary2cart(M,Tr)} #need to run this when M is given in barycentric coordinates
plot(Tr,pch=".",xlab="",ylab="",
main="Projection of Center M to the edges of a triangle",axes=TRUE,
xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05))
polygon(Tr)
L<-rbind(M,M,M); R<-Ds
segments(L[,1], L[,2], R[,1], R[,2], lty=2)
xc<-Tr[,1]
yc<-Tr[,2]
txt.str<-c("rv=1","rv=2","rv=3")
text(xc,yc,txt.str)
txt<-rbind(M,Ds)
xc<-txt[,1]+c(-.02,.04,-.04,-.02)
yc<-txt[,2]+c(-.02,.04,.04,-.06)
txt.str<-c("M","D1","D2","D3")
text(xc,yc,txt.str)
The function prj.cent2edges.basic.tri is a special case of prj.cent2edges
taking arguments c1,c2 instead of tri
and returning projections of $M$ to the edges in the standard basic triangle.
The function prj.nondegPEcent2edges takes arguments
tri, a $3 \times 2$ matrix with each row representing a vertex of the triangle.r, a positive real number which serves as the expansion parameter in PE proximity region,
must be in $(1,1.5]$ for this function.cent, an index of the center (as $1,2,3$ corresponding to $M_1,\,M_2,\,M_3$) which gives nondegenerate asymptotic
distribution of the domination number of PE-PCD for uniform data in tri for expansion parameter r in $(1,1.5]$;
default cent=1.
This function returns the projections of nondegeneracy centers
(i.e. centers for non-degenerate asymptotic distribution of domination number of
PE-PCDs) to its edges,
i.e., it returns the intersection point where the line joining a vertex to a non-degeneracy center $M_i$ crosses the opposite edge.
The line segments between $M_i$ the intersection points will provide the $M_i$-vertex regions.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
Tr<-rbind(A,B,C);
r<-1.35
prj.nondegPEcent2edges(Tr,r,cent=2)
#> [,1] [,2]
#> [1,] 1.825 0.70
#> [2,] 1.250 1.50
#> [3,] 1.650 0.35
We plot the projections of the non-degeneracy center $M_1$ to the edges of a triangle using the below code,
type also ? prj.nondegPEcent2edges for the code to generate this figure.
Ms<-center.nondegPE(Tr,r)
M1=Ms[1,]
Ds<-prj.nondegPEcent2edges(Tr,r,cent=1)
Xlim<-range(Tr[,1])
Ylim<-range(Tr[,2])
xd<-Xlim[2]-Xlim[1]
yd<-Ylim[2]-Ylim[1]
plot(Tr,pch=".",xlab="",ylab="",
main="Projections from a non-degeneracy center for domination number\n of PE-PCD to the edges of the triangle",
axes=TRUE,xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05))
polygon(Tr)
points(Ms,pch=".",col=1)
polygon(Ms,lty=2)
xc<-Tr[,1]+c(-.02,.03,.02)
yc<-Tr[,2]+c(-.02,.04,.04)
txt.str<-c("A","B","C")
text(xc,yc,txt.str)
txt<-Ms
xc<-txt[,1]+c(-.02,.04,-.04)
yc<-txt[,2]+c(-.02,.04,.04)
txt.str<-c("M1","M2","M3")
text(xc,yc,txt.str)
points(Ds,pch=4,col=2)
L<-rbind(M1,M1,M1); R<-Ds
segments(L[,1], L[,2], R[,1], R[,2], lty=2,lwd=2,col=4)
txt<-Ds
xc<-txt[,1]+c(-.02,.04,-.04)
yc<-txt[,2]+c(-.02,.04,.04)
txt.str<-c("D1","D2","D3")
text(xc,yc,txt.str)
The function in.triangle takes arguments
p, a 2D point to be checked whether it is inside the triangle or not,tri, a $3 \times 2$ matrix with each row representing a vertex of the triangle,boundary, a logical parameter (default=FALSE) to include boundary or not, so if it is TRUE,
the function checks if the point, p, lies in the closure of the triangle (i.e., interior and boundary
combined) else it checks if p lies in the interior of the triangle.
This function can be used to check whether a point p is inside a triangle tri or not.
On the other hand in.tri.all takes arguments Xp,tri,boundary where
Xp is a 2D data set and tri,boundary are as in the function in.triangle.
This function checks whether all of the points in a data set are inside the triangle or not.
We check in.tri.all with $n=5$ data points generated uniformly in the unit square and in the triangle.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
p<-c(1.4,1.2)
Tr<-rbind(A,B,C)
in.triangle(p,Tr)
#> $in.tri
#> [1] TRUE
#>
#> $barycentric
#> [1] 0.4 0.2 0.4
#data set and checking all points in it are inside the triangle or not
n<-5
Xp1<-cbind(runif(n),runif(n))
in.tri.all(Xp1,Tr)
#> [1] FALSE
The function is.std.eq.tri takes the argument tri (a $3 \times 2$ matrix for a triangle) and
can be used for checking the triangle tri is standard equilateral triangle or not,
regardless of the order of the vertices.
A<-c(0,0); B<-c(1,0); C<-c(1/2,sqrt(3)/2);
Te<-rbind(A,B,C) #try adding +10^(-16) to each vertex
is.std.eq.tri(Te)
#> [1] TRUE
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
Tr<-rbind(A,B,C);
is.std.eq.tri(Tr)
#> [1] FALSE
The function as.basic.tri takes the arguments tri,scaled where
-tri is as in is.std.eq.tri and
-scaled is a logical argument for scaling the resulting triangle. If scaled=TRUE, then the resulting triangle is
scaled to be a regular basic triangle, i.e., longest edge having unit length,
else the new triangle $T(A,B,C)$ is nonscaled. The default is scaled=FALSE.
It converts any triangle to a basic triangle (up to translation and rotation),
so that the output triangle is $T(A',B',C')$ so that edges in decreasing length are $A'B'$, $B'C'$, and $A'C'$.
The option scaled scales the output triangle when scaled=TRUE so that longest edge $A'B'$ has unit length
(default is scaled=FALSE).
Most of the times,
the resulting triangle will still need to be translated and/or rotated to be in the standard basic triangle form.
c1<-.4; c2<-.6
A<-c(0,0); B<-c(1,0); C<-c(c1,c2);
as.basic.tri(rbind(B,C,A))
#> $tri
#> [,1] [,2]
#> A 0.0 0.0
#> B 1.0 0.0
#> C 0.4 0.6
#>
#> $desc
#> [1] "Edges (in decreasing length are) AB, BC, and AC"
#>
#> $orig.order
#> [1] 3 1 2
x<-c(1,1); y<-c(2,0); z<-c(1.5,2);
as.basic.tri(rbind(x,y,z))
#> $tri
#> [,1] [,2]
#> A 1.5 2
#> B 2.0 0
#> C 1.0 1
#>
#> $desc
#> [1] "Edges (in decreasing length are) AB, BC, and AC"
#>
#> $orig.order
#> [1] 3 2 1
as.basic.tri(rbind(x,y,z),scaled = TRUE)
#> $tri
#> [,1] [,2]
#> A 0.7276069 0.9701425
#> B 0.9701425 0.0000000
#> C 0.4850713 0.4850713
#>
#> $desc
#> [1] "Edges (in decreasing length are) AB, BC, and AC"
#>
#> $orig.order
#> [1] 3 2 1
The function tri2std.basic.tri converts a triangle to the standard basic triangle form,
and its output only returns $c_1,c_2$ in $Cvec and
also the original order of the vertices in the input triangle
by $orig.order.
c1<-.4; c2<-.6
A<-c(0,0); B<-c(1,0); C<-c(c1,c2);
tri2std.basic.tri(rbind(B,C,A))
#> $Cvec
#> [1] 0.4 0.6
#>
#> $orig.order
#> [1] 3 1 2
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
tri2std.basic.tri(rbind(A,B,C))
#> $Cvec
#> [1] 0.4117647 0.3529412
#>
#> $orig.order
#> [1] 3 2 1
Barycentric coordinates are very useful in defining and analyzing the triangular (and simplicial) proximity regions.
So, we provide functions to convert barycentric coordinates to Cartesian coordinates (bary2cart) and vice versa (cart2bary).
Both functions take the arguments P,tri where P is th point to change the coordinates for and tri is the reference triangle
(as a $3\times2$ matrix).
As the names suggest,
the function cart2bary converts the point P in Cartesian coordinates
to barycentric coordinates with respect to the triangle tri,
and bary2cart converts the point P in barycentric coordinates with respect to the triangle tri to Cartesian coordinates.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
Tr<-rbind(A,B,C);
cart2bary(c(1.4,1.2),Tr)
#> [1] 0.4 0.2 0.4
bary2cart(c(1.4,1.2,1),Tr)
#> [1] 1.4722222 0.9444444
CM<-(A+B+C)/3; CM
#> [1] 1.5 1.0
cart2bary(CM,Tr)
#> [1] 0.3333333 0.3333333 0.3333333
bary2cart(c(1,1,1),Tr)
#> [1] 1.5 1.0
The function index.delaunay.tri takes the arguments
p, a 2D point; the index of the Delaunay triangle in which p resides is to be determined.
It is an argument for index.delaunay.tri.Yp, a set of 2D points from which Delaunay triangulation is constructed,DTmesh, Delaunay triangles based on Yp, default is NULL,
which is computed via interp::tri.mesh function
in interp package. interp::triangles function returns a triangulation data structure from the triangulation object
created by tri.mesh.
This function returns the index of the Delaunay triangle in which the given point resides.
Similarly, the function indices.delaunay.tri takes the arguments Xp,Yp,DTmesh where
Xp is the set of data points for which the indices of the Delaunay
triangles they reside is to be determined and Yp,DTmesh are as in index.delaunay.tri.
This function returns the indices of triangles for all the points in a data set as a vector.
nx<-10 #number of X points (target)
ny<-5 #number of Y points (nontarget)
We generate $n_x=$ r nx $\X$ points uniformly in the convex hull of $n_y=$ r ny $\Y$ points
using the function runif.multi.tri in pcds package.
set.seed(1)
Yp<-cbind(runif(ny),runif(ny))
Xp<-runif.multi.tri(nx,Yp)$g #data under CSR in the convex hull of Ypoints
#try also Xp<-cbind(runif(nx),runif(nx))
index.delaunay.tri(Xp[10,],Yp)
#> [1] 2
#or use
DTY<-interp::tri.mesh(Yp[,1],Yp[,2],duplicate="remove") #Delaunay triangulation
index.delaunay.tri(Xp[10,],Yp,DTY)
#> [1] 2
(tr.ind<-indices.delaunay.tri(Xp,Yp,DTY)) #indices of the Delaunay triangles
#> [1] 3 3 1 4 3 2 3 3 2 2
We provide the scatterplot of $\X$ points (labeled according to the Delaunay triangle they reside in),
and the Delaunay triangulation
of $n_y=$ r ny $\Y$ points using the below code. Type also ? indices.delaunay.tri.
Xlim<-range(Yp[,1],Xp[,1])
Ylim<-range(Yp[,2],Xp[,2])
xd<-Xlim[2]-Xlim[1]
yd<-Ylim[2]-Ylim[1]
# plot of the data in the convex hull of Y points together with the Delaunay triangulation
#par(pty="s")
plot(Xp,main="X Points in Delaunay Triangles for Y Points", xlab=" ", ylab=" ",
xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05),pch=".")
interp::plot.triSht(DTY, add=TRUE, do.points = TRUE,pch=16,col="blue")
text(Xp,labels = factor(tr.ind) )
Functions Related to the Vertex Regions
The function rel.vert.tri takes arguments p,tri,M
where p is a 2D point for which M-vertex region it resides in is to be determined in the
triangle tri and tri,M are for the triangle and the center used to construct the vertex regions, respectively.
This function returns rv, the index (i.e., the row number in tri) of the vertex of tri whose region contains the point p
and the vertices of the tri.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
Tr<-rbind(A,B,C);
M<-c(1.6,1.0)
P<-c(1.8,.5)
rel.vert.tri(P,Tr,M)
#> $rv
#> [1] 2
#>
#> $tri
#> [,1] [,2]
#> vertex 1 1.0 1
#> vertex 2 2.0 0
#> vertex 3 1.5 2
n<-5 #try also n<-10, 50, or 100
We illustrate the vertex regions using the following code.
We annotate the vertices with corresponding indices with rv=i for $i=1,2,3$,
and also generate $n=$ r n points inside the triangle and
provide the scatterplot of these points labeled according to the vertex region
they reside in.
Type also ? rel.vert.tri for the code to generate this figure.
set.seed(1)
Xp<-runif.tri(n,Tr)$g
M<-c(1.6,1.0) #try also M<-as.numeric(runif.tri(1,Tr)$g)
Rv<-vector()
for (i in 1:n)
{Rv<-c(Rv,rel.vert.tri(Xp[i,],Tr,M)$rv)}
Rv
Ds<-prj.cent2edges(Tr,M)
Xlim<-range(Tr[,1],Xp[,1])
Ylim<-range(Tr[,2],Xp[,2])
xd<-Xlim[2]-Xlim[1]
yd<-Ylim[2]-Ylim[1]
if (dimension(M)==3) {M<-bary2cart(M,Tr)} #need to run this when M is given in barycentric coordinates
plot(Tr,pch=".",xlab="",ylab="",main="Illustration of M-Vertex Regions\n in a Triangle",axes=TRUE,
xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05))
polygon(Tr)
points(Xp,pch=".",col=1)
L<-rbind(M,M,M); R<-Ds
segments(L[,1], L[,2], R[,1], R[,2], lty=2)
xc<-Tr[,1]
yc<-Tr[,2]
txt.str<-c("rv=1","rv=2","rv=3")
text(xc,yc,txt.str)
txt<-rbind(M,Ds)
xc<-txt[,1]+c(-.02,.04,-.04,0)
yc<-txt[,2]+c(-.02,.04,.05,-.08)
txt.str<-c("M","D1","D2","D3")
text(xc,yc,txt.str)
text(Xp,labels=factor(Rv))
The following functions are special cases of rel.vert.tri, where
rel.vert.basic.tri takes c1,c2 for tri and returns the same output for the standard basic triangle,rel.vert.basic.triCM takes arguments p,c1,c2 and returns the indices of the points
with respect to CM-vertex regions in the standard basic triangle,rel.vert.triCM takes arguments p,tri and returns the indices of the points with respect to CM-vertex regions in a triangle,rel.vert.std.tri takes arguments p,M and returns the indices of the points with respect to $M$-vertex regions
in the standard equilateral triangle,
andrel.vert.std.triCM takes argument p and returns the indices of the points with respect to CM-vertex regions
in the standard equilateral triangle.
The function rel.verts.tri takes arguments Xp,tri,M for the data set, triangle,
and the center, respectively,
and returns the indices of the $M$-vertex regions in a triangle
where the points in a give data set reside in.
This function is the multiple point (or set) version of rel.vert.tri.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
Tr<-rbind(A,B,C);
n<-5 #try also n<-10, 50, or 100
set.seed(1)
Xp<-runif.tri(n,Tr)$g
M<-c(1.6,1.0) #try also M<-as.numeric(runif.tri(1,Tr)$g)
(rv<-rel.verts.tri(Xp,Tr,M))
#> $rv
#> [1] 1 2 3 1 1
#>
#> $tri
#> [,1] [,2]
#> vertex 1 1.0 1
#> vertex 2 2.0 0
#> vertex 3 1.5 2
We can generate $n=$ r n points inside the triangle and
provide the scatterplot of these points labeled according to the vertex region
they reside in,
and also plot the vertex regions.
The resulting figure will be identical to the one in the examples of rel.vert.tri
Type ? rel.verts.tri for the code to generate the corresponding figure.
The following functions are special cases of rel.verts.tri, where
rel.verts.tri takes the same arguments and returns the same output as
and is an alternate to rel.verts.tri when $M$ is not the circumcenter,rel.verts.tri.nondegPE takes arguments Xp,tri,r,cent=1 for the data set, triangle, the expansion parameter,
and the non-degeneracy center (with default being the first center), respectively,
and returns the indices of the data points with respect to M-vertex regions in a triangle
where $M$ is one of the non-degeneracy centers of the triangle,
andrel.verts.triCM takes the arguments Xp,tri and
returns the indices of the data points with respect to CM-vertex regions in a triangle.
The $M$-vertex regions for any $M$ in the interior of the triangle is by default defined with the extensions of the
lines combining the vertices to the center $M$ (i.e., projections of M to the edges) as in the function prj.cent2edges.
Such vertex regions are only defined for a central point $M$ in the interior of the triangle.
However, using the circumcenter (CC), we define the vertex regions with the orthogonal projections from CC to the edges
(as this is a natural construction for the CC-vertex regions, because the corresponding partition of the triangle constitutes
vertex regions, where each vertex region contains points in the triangle closest to that vertex).
Note also that CC can be inside, on the boundary, or outside of the triangle
when the triangle is acute, right, or obtuse, respectively.
The function rel.vert.triCC(P,Tr) takes arguments p,tri and
returns the index of the CC-vertex region in a triangle tri where the point p resides.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
Tr<-rbind(A,B,C);
P<-c(1.3,1.2)
rel.vert.triCC(P,Tr)
#> $rv
#> [1] 1
#>
#> $tri
#> [,1] [,2]
#> vertex 1 1.0 1
#> vertex 2 2.0 0
#> vertex 3 1.5 2
We can illustrate the CC-vertex regions in an annotated plot,
type ? rel.vert.triCC for the code to generate this plot.
This plot will be as in the one in the examples of rel.verts.triCC without the scatter plot of the data points.
The function rel.vert.basic.triCC is a special case of the rel.vert.triCC function,
takes the arguments p,c1,c2 and return
the CC-vertex regions in the standard basic triangle.
The function rel.verts.triCC takes arguments Xp,tri
and returns the indices of the CC-vertex regions in a triangle tri where the points in a give data set Xp reside in.
This function is the multiple point (or set) version of rel.vert.triCC.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
Tr<-rbind(A,B,C);
n<-5 #try also n<-10, 50, or 100
set.seed(1)
Xp<-runif.tri(n,Tr)$g
(rv<-rel.verts.triCC(Xp,Tr))
#> $rv
#> [1] 1 1 3 1 1
#>
#> $tri
#> [,1] [,2]
#> vertex 1 1.0 1
#> vertex 2 2.0 0
#> vertex 3 1.5 2
We generate $n=$ r n points inside the triangle and provide the scatterplot of these points
labeled according to the CC-vertex region they reside in,
and also plot the CC-vertex regions using the below code.
Type also ? rel.verts.triCC for the code to generate this figure.
CC<-circumcenter.tri(Tr)
D1<-(B+C)/2; D2<-(A+C)/2; D3<-(A+B)/2;
Ds<-rbind(D1,D2,D3)
Xlim<-range(Tr[,1],Xp[,1],CC[1])
Ylim<-range(Tr[,2],Xp[,2],CC[2])
xd<-Xlim[2]-Xlim[1]
yd<-Ylim[2]-Ylim[1]
plot(Tr,pch=".",asp=1,xlab="",ylab="",
main="Scatterplot of data points with the CC-vertex regions",
axes=TRUE,xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05))
polygon(Tr)
points(Xp,pch=".",col=1)
L<-matrix(rep(CC,3),ncol=2,byrow=TRUE); R<-Ds
segments(L[,1], L[,2], R[,1], R[,2], lty=2)
xc<-Tr[,1]
yc<-Tr[,2]
txt.str<-c("rv=1","rv=2","rv=3")
text(xc,yc,txt.str)
txt<-rbind(CC,Ds)
xc<-txt[,1]+c(.04,.04,-.03,0)
yc<-txt[,2]+c(-.07,.04,.06,-.08)
txt.str<-c("CC","D1","D2","D3")
text(xc,yc,txt.str)
text(Xp,labels=factor(rv$rv))
Functions Related to the Edge Regions
The function rel.edge.tri takes arguments p,tri,M where
p, is a 2D point for which M-edge region it resides in is to be determined in the triangle tri,tri, is a $3 \times 2$ matrix with each row representing a vertex of the triangle.M is 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.
This function returns the index of the M-edge region in a triangle tri that contains the point p.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
Tr<-rbind(A,B,C);
P<-c(1.4,1.2)
M<-c(1.6,1.2) #try also set.seed(1234); M<-as.numeric(runif.tri(1,Tr)$g)
rel.edge.tri(P,Tr,M)
#> $re
#> [1] 2
#>
#> $tri
#> [,1] [,2]
#> A 1.0 1
#> B 2.0 0
#> C 1.5 2
#>
#> $desc
#> [1] "Edge labels are AB=3, BC=1, and AC=2"
We can illustrate the CC-vertex regions in an annotated plot,
type ? rel.edge.tri for the code to generate this plot.
This plot will be as in the examples of rel.edges.tri without the scatter plot of the data points.
The following functions are special cases of rel.edge.tri, where
rel.edge.basic.tri takes the arguments p,c1,c2,M and returns the same output for the standard basic triangle,rel.edge.basic.triCM takes the arguments p,c1,c2 and returns the indices of the points
with respect to CM-edge regions in the standard basic triangle,edge.reg.triCM takes the arguments p,tri and returns the triangular edge region for a given data point,
andrel.edge.triCM takes the arguments p,tri and returns the indices of the points with respect to CM-edge regions in a triangle tri.
The function rel.edges.tri takes arguments Xp,tri,M and
returns the indices of the $M$-edge regions in a triangle tri where the points in a give data set Xp reside in.
This function is the multiple point (i.e. set of points) version of rel.edge.tri.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2);
Tr<-rbind(A,B,C);
n<-5 #try also n<-10, 50, or 100
set.seed(1)
Xp<-runif.tri(n,Tr)$g
M<-c(1.6,1.2) #try also M<-as.numeric(runif.tri(1,Tr)$g)
(re<-rel.edges.tri(Xp,Tr,M))
#> $re
#> [1] 3 3 2 3 2
#>
#> $tri
#> [,1] [,2]
#> A 1.0 1
#> B 2.0 0
#> C 1.5 2
#>
#> $desc
#> [1] "Edge labels are AB=3, BC=1, and AC=2"
We generate $n=$ r n points inside the triangle and provide the scatterplot of these points
labeled according to the edge region they reside in,
and also plot the edge regions using the below code.
Type also ? rel.edges.tri for the code to generate this figure.
D1<-(B+C)/2; D2<-(A+C)/2; D3<-(A+B)/2;
Ds<-rbind(D1,D2,D3)
Xlim<-range(Tr[,1],Xp[,1])
Ylim<-range(Tr[,2],Xp[,2])
xd<-Xlim[2]-Xlim[1]
yd<-Ylim[2]-Ylim[1]
if (dimension(M)==3) {M<-bary2cart(M,Tr)} #need to run this when M is given in barycentric coordinates
plot(Tr,pch=".",xlab="",ylab="",
main="Scatterplot of data points with the M-edge regions",axes=TRUE,
xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05))
polygon(Tr)
points(Xp,pch=".",col=1)
L<-Tr; R<-rbind(M,M,M)
segments(L[,1], L[,2], R[,1], R[,2], lty=2)
xc<-Tr[,1]+c(-.02,.03,.02)
yc<-Tr[,2]+c(.02,.02,.04)
txt.str<-c("A","B","C")
text(xc,yc,txt.str)
txt<-rbind(M,Ds)
xc<-txt[,1]+c(.05,.06,-.05,-.02)
yc<-txt[,2]+c(.03,.03,.05,-.08)
txt.str<-c("M","re=1","re=2","re=3")
text(xc,yc,txt.str)
text(Xp,labels=factor(re$re))
The function rel.edges.triCM is a special case of rel.edges.tri, and uses the center of mass
to construct the edge regions.
References
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knitr::opts_chunk$set(collapse = TRUE,comment = "#>",fig.width=6, fig.height=4, fig.align = "center")
\newcommand{\X}{\mathcal{X}} \newcommand{\Y}{\mathcal{Y}}
First we load the pcds package:
library(pcds)
Functions for Construction and Simulation of PCDs and Related Quantities
For illustration of construction of PCDs and related quantities, we first focus on one Delaunay cell, because subdigraph of the PCDs with vertices in one Delaunay cell are disconnected from the remaining vertices. Thus arc density and domination number of PCDs can be obtained by adding numbers of arcs and domination numbers for the subdigraphs for Delaunay cells. A Delaunay cell is an interval for 1D space, a triangle for 2D space, and a tetrahedron for 3D space. The arc density and the domination number of PE- and CS-PCDs for uniform data in two or higher dimensions, is geometry invariant, thus we may also focus on standard equilateral triangle in 2D space and a standard regular tetrahedron in the 3D space. Geometry invariance means that the distribution of arc density or domination number of these PCDs does not depend on the form (lengths of edges and inner angles) of the triangle or (lengths of edges, areas of faces and inner angles) of the tetrahedron for uniform data. So, one can use the standard Delaunay cells for computations and also simulations to utilize symmetry as the results extend to any type of Delaunay cell for uniform data. For one dimensional uniform data, the PCDs enjoy scale invariance, which means that the distribution of arc density and domination number does not depend on the length of the interval support. Thus, we present functions for a single Delaunay cell (and also for the standard Delaunay cells) in "Section VS2*" files.
Functions for PCDs with Vertices in One Triangle
Due to geometry invariance of PE- and CS-PCDs for uniform data (which will be the vertices of the PCDs) in any triangle in 2D space, most computations (and if needed data generation and simulations) can be done in the standard equilateral triangle, and for AS-PCD, one can restrict attention to the standard basic triangle. The standard equilateral triangle is $T_e=T(A,B,C)$ with vertices $A=(0,0)$, $B=(1,0)$, and $C=(1/2,\sqrt{3}/2)$ and the standard basic triangle is $T_b=T(A,B,C')$ with vertices $A=(0,0)$, $B=(1,0)$, and $C'=(c_1,c_2))$ with $0 < c_1 \le 1/2$, $c_2>0$, and $(1-c_1)^2+c_2^2 \le 1$.
Most of the PCD functions we will illustrate in this section are counterparts of the functions in Section "VS1_1_2DArtiData" (i.e. one-interval counterparts of the functions for the multiple-triangle setting). Sometimes we will be focusing on the standard equilateral triangle (for speed and ease of computation).
For more detail on the construction and appealing properties of PCDs in triangles, see @ceyhan:comp-geo-2010 and @ceyhan:mcap2012.
We first choose an arbitrary triangle $T=T(A,B,C)$ with vertices $A=(1,1)$, $B=(2,0)$, and $C=(1.5,2)$, which happens to be an obtuse triangle, and choose an arbitrary point in the interior of this triangle as the center (to construct the vertex or edge regions).
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); n<-5 set.seed(1) Xp<-runif.tri(n,Tr)$g #try also Xp<-cbind(runif(n,1,2),runif(n,0,2)) M<-c(1.6,1.2) #try also M<-as.numeric(runif.tri(1,Tr)$g) or M="CC"
Then we generate $n=$ r n $\X$ points inside the triangle $T$ using the function runif.tri in pcds.
$\X$ points are denoted as Xp and $\Y$ points in Section "VS1_1_2DArtiData"
correspond to the vertices of the triangle $T$.
Hence, if the argument Yp is used for a function for multiple triangles,
it is replaced with tri a $3 \times 2$ matrix in which rows represent the vertices of the triangle
for a function written for a general triangle,
with c1,c2 for a basic triangle,
or omitted for the standard equilateral triangle.
We plot the triangle $T$ and the $\X$ points in it using the below code,
and also add the vertex names using the text function from base R.
Xlim<-range(Tr[,1]) Ylim<-range(Tr[,2]) plot(Tr,pch=".",xlab="",ylab="",xlim=Xlim,ylim=Ylim+c(0,.1),main="Points in One Triangle") polygon(Tr) points(Xp) #add the vertex names and annotation txt<-rbind(Tr) xc<-txt[,1]+c(-.01,.015,.02) yc<-txt[,2]+c(.02,.02,.02) txt.str<-c("A","B","C") text(xc,yc,txt.str)
Alternatively, we can use the plotDelaunay.tri function in pcds to obtain the same plot by executing plotDelaunay.tri(Xp,Tr,xlab="x",ylab="y").
Functions for Arc-Slice PCDs with Vertices in One Triangle {#sec:AS-PCD-one-tri}
Arc-Slice (AS) proximity region for a point is the intersection of the triangle containing the point
and the circle centered the point with radius being
the minimum distance from the point to the vertices of the triangle.
So, we first check whether a point is inside a circle or not by using the function in.circle which returns TRUE
if the point is inside the circle, and FALSE otherwise.
This function takes arguments p,cent,rad,boundary
where
pis a 2D point to be checked whether it is inside the circle or not,centa 2D point in Cartesian coordinates which serves as the center of the circle,rada positive real number which serves as the radius of the circle,boundarya logical parameter (default=TRUE) to include boundary or not, so if it isTRUE, the function checks if the point,p, lies in the closure of the circle (i.e., interior and boundary combined); else, it checks ifplies in the interior of the circle.
cent<-c(1,1); rad<-1; p<-c(1.4,1.2) #try also cent<-runif(2); rad<-runif(1); p<-runif(2); in.circle(p,cent,rad) p<-c(.4,-.2) in.circle(p,cent,rad) #> [1] TRUE #> [1] FALSE
The circle for an AS proximity region of an $\X$ point $x$ is centered at the point $x$
with radius being the distance from $x$ to the closest class $\Y$ point
(which is the closest vertex of the triangle $T$ in the one triangle setting).
The radius of a point from one class with respect to points from the other class is provided by the function radius
which takes arguments p,Y where
pis the point to find the radius for andYis the matrix of non-target points.
The function also returns the index and coordinates of the class $\Y$ point closest to the class $\X$ point.
This function works for points in other dimensions as well.
See ? radius for further description.
ny<-5 Y<-cbind(runif(ny),runif(ny)) A<-c(1,1); radius(A,Y) #> $rad #> [1] 0.3767951 #> #> $index.of.clYpnt #> [1] 4 #> #> $closest.Ypnt #> [1] 0.6684667 0.8209463
The radii of points from one class with respect to points from the other class are found by using the function radii
which takes arguments x,y
where
xis a set of $d$-dimensional points for which the radii are computed. Radius of anxpoint equals to the distance to the closestypoint andyis a set of $d$-dimensional points representing the reference points for the balls. That is, radius of anxpoint is defined as the minimum distance to theypoints.
In addition to the radii, the function also returns the indices and coordinates of the class $\Y$ points closest to the class $\X$ points.
This function works for points in other dimensions as well.
See ? radii for further description.
nx<-6 ny<-5 X<-cbind(runif(nx),runif(nx)) Y<-cbind(runif(ny),runif(ny)) Rad<-radii(X,Y) Rad #> $radiuses #> [1] 0.38015546 0.23618472 0.02161322 0.48477828 0.05674010 0.16819521 #> #> $indices.of.closest.points #> [1] 4 4 4 4 1 3 #> #> $closest.points #> [,1] [,2] #> [1,] 0.51863426 0.4590657 #> [2,] 0.51863426 0.4590657 #> [3,] 0.51863426 0.4590657 #> [4,] 0.51863426 0.4590657 #> [5,] 0.07067905 0.4068302 #> [6,] 0.31627171 0.2936034
The function NAStri is used for the construction of AS proximity regions taking the arguments
pthe point for which the AS proximity region is to be found,trithe support triangle,Mis the center to construct the vertex regions with defaultM="CC"(i.e. the circumcenter),rvthe index of the vertex regions that the pointpresides in with defaultrv=NULLanddeca positive integer the number of decimals (default is 4) to round the barycentric coordinates when checking whether the end points fall on the boundary of the triangletrior not.
NAStri returns the proximity region as end points of the straight line segments on the boundary of the proximity region
(which also fall on the boundary of the triangle),
the end points of the arc slices which are the parts of the defining circle falling in the interior of the triangle
and the angles between the vectors joining P and the end points of the arc slices and the horizontal line crossing the point P.
It is also possible to specify the index of the vertex region for the point P with the argument rv=k for $k=1,2,3$,
where $k$ refers to the vertex in $k$-th row for the triangle Tr
(but this must be computed before such as in the code Rv<-rel.vert.triCC(P1,Tr)$rv; NAStri(P1,Tr,M,Rv)
and it must be compatible with
the vertex region for the point P).
P<-c(1.8,.5) NAStri(P,Tr,M) #> $L #> [,1] [,2] #> [1,] 2 0 #> [2,] 2 0 #> #> $R #> [,1] [,2] #> [1,] 1.741176 1.035294 #> [2,] 1.300000 0.700000 #> #> $arc.slices #> [,1] [,2] #> [1,] 1.741176 1.035294 #> [2,] 1.300000 0.700000 #> #> $Angles #> [1] 1.680247 2.761086
Indicator for the presence of an arc from a (data or $\X$) point to another for AS-PCDs is the function IarcAStri.
One can use it for points in the data set or for arbitrary points (as if they were in the data set).
It takes the arguments,
p1a 2D point whose AS proximity region is constructed,p2another 2D point. The function determines whetherp2is inside the AS proximity region ofp1or not,trivertices of the support triangle, stacked row-wise, i.e., each row representing a vertex of the triangle,Mthe center of the triangle to construct the vertex regions with, default isM="CC"i.e., the circumcenter oftriandrvthe index of theM-vertex region intricontaining the point, either 1,2,3 orNULL(which is the default).
This function returns $I(p2 \in N_{AS}(p1))$, that is, returns 1 if p2 is in $N_{AS}(p1)$, 0 otherwise.
#between two arbitrary points P1 and P2 P1<-as.numeric(runif.tri(1,Tr)$g) P2<-as.numeric(runif.tri(1,Tr)$g) IarcAStri(P1,P2,Tr,M) #> [1] 0 #between the first two points in Xp IarcAStri(Xp[1,],Xp[2,],Tr,M) #> [1] 0
AS proximity regions are defined with respect to the
vertices of the triangle (i.e., with respect to the vertex regions they reside in) and
vertex regions in each triangle are based on the center M
for circumcenter or $M=(\alpha,\beta,\gamma)$ in barycentric coordinates in the
interior of the triangle; default is M="CC" i.e., circumcenter of the triangle.
See @ceyhan:Phd-thesis, @ceyhan:comp-geo-2010, and @ceyhan:mcap2012 for more on AS-PCDs.
Number of arcs of the AS-PCD can be computed by the function num.arcsAStri.
The function num.arcsAStri is an object of class "NumArcs"
and takes Xp,tri,M="CC" as its arguments
where Xp is the data set, and the others are as above
and returns the list of
desc: A description of the PCD and the outputnum.arcs: Number of arcs of the AS-PCD,num.in.tri: Number ofXppoints in the triangletri,ind.in.tri: The vector of indices of theXppoints that reside in the triangle.tess.points: vertices of the support triangle (i.e.Yppoints)vertices: vertices of the PCD (i.e.Xppoints)
Narcs = num.arcsAStri(Xp,Tr) #with default M="CC"; try also num.arcsAStri(Xp,Tr,M) summary(Narcs) #> Call: #> num.arcsAStri(Xp = Xp, tri = Tr) #> #> Description of the output: #> Number of Arcs of the AS-PCD and the Related Quantities with vertices Xp in One Triangle #> #> Number of data (Xp) points in the triangle = 5 #> Number of arcs in the digraph = 10 #> #> Indices of data points in the triangle: #> 1 2 3 4 5 #> #plot(Narcs)
The arc density of the AS-PCD can be computed by the function ASarc.dens.tri.
It takes the arguments Xp,tri,M="CC",tri.cor where Xp is the data set (or $\X$ points)
tri,M="CC" are as above.
Arc density can be adjusted (or corrected) for the points outside the triangle by using the option tri.cor=T,
default option is tri.cor=FALSE.
ASarc.dens.tri(Xp,Tr,M) #> [1] 0.5
The incidence matrix of the AS-PCD for the one triangle case can be found by inci.matAStri,
using the inci.matAStri(Xp,Tr,M) command.
It takes the same arguments as the function num.arcsAStri.
Plot of the arcs in the digraph AS-PCD, which is based on the $M$-vertex regions with $M=($ r M[1],r M[2] $)$,
can be obtained by the function plotASarcs.tri, which is the one-tri counterpart of the function plotASarcs.
It takes arguments Xp,tri,M="CC" (as in num.arcsAStri) and other options for the plot function.
See the help page for the function using ? plotASarcs.tri.
Plot of the arcs of the above AS-PCD,
together with the vertex regions (with the option vert.reg = TRUE).
plotASarcs.tri(Xp,Tr,M,xlab="",ylab="",vert.reg = TRUE)
Or, one can use the default center M="CC", and can add vertex names and text to the figure (with vertex regions)
using the below code.
The part M = as.numeric(arcsAStri(Xp,Tr)$param) is optional,
for the below annotation of the plot since circumcenter is used for the vertex regions.
par(pty = "s") plotASarcs.tri(Xp,Tr,asp=1,xlab="",ylab="",vert.reg = TRUE); M = (arcsAStri(Xp,Tr)$param)$c CC<-circumcenter.tri(Tr) #determine whether the center used for vertex regions is circumcenter or not if (identical(M,CC) || identical(M,"CC")) {cent<-CC D1<-(B+C)/2; D2<-(A+C)/2; D3<-(A+B)/2; Ds<-rbind(D1,D2,D3) cent.name<-"CC" } else {cent<-M cent.name<-"M" Ds<-prj.cent2edges(Tr,M) } #add the vertex names and annotation txt<-rbind(Tr,cent,Ds) xc<-txt[,1]+c(-.02,.02,.02,.05,.05,-0.03,-.01) yc<-txt[,2]+c(.02,.02,.02,.07,.02,.05,-.06) txt.str<-c("A","B","C",cent.name,"D1","D2","D3") text(xc,yc,txt.str)
Plot of the AS proximity regions can be obtained by the function plotASregs.tri.
It takes arguments Xp,tri,M="CC" (as in num.arcsAStri) and other options for the plot function.
See the help page for the function using ? plotASregs.tri.
M<-c(1.6,1.2) #try also M<-c(1.6620051,0.8136604) or M="CC" par(pty = "s") plotASregs.tri(Xp,Tr,M,vert.reg = T,xlab="",ylab="")
The function arcsAStri is an object of class "PCDs".
It takes arguments Xp,tri,M="CC" (as in ASarc.dens.tri).
The output list is as in the function arcsAS
(see Section "VS1_1_2DArtiData").
The plot function returns the same plot as in plotASarcs.tri with $M=($ r M[1],r M[2] $)$,
hence we comment it out below.
M=c(1.6,1.2) #try also M=c(1.6620051,0.8136604) Arcs<-arcsAStri(Xp,Tr,M) #try also Arcs<-arcsAStri(Xp,Tr) #uses the default center, namely circumcenter for M Arcs #> Call: #> arcsAStri(Xp = Xp, tri = Tr, M = M) #> #> Type: #> [1] "Arc Slice Proximity Catch Digraph (AS-PCD) for 2D Points in the Triangle with Center M = (1.6,1.2)" summary(Arcs) #> Call: #> arcsAStri(Xp = Xp, tri = Tr, M = M) #> #> Type of the digraph: #> [1] "Arc Slice Proximity Catch Digraph (AS-PCD) for 2D Points in the Triangle with Center M = (1.6,1.2)" #> #> Vertices of the digraph = Xp #> Partition points of the region = Tr #> #> Selected tail (or source) points of the arcs in the digraph #> (first 6 or fewer are printed) #> [,1] [,2] #> [1,] 1.265509 0.7442478 #> [2,] 1.687023 0.7682074 #> [3,] 1.687023 0.7682074 #> [4,] 1.687023 0.7682074 #> [5,] 1.380035 1.5548904 #> [6,] 1.267221 0.7722282 #> #> Selected head (or end) points of the arcs in the digraph #> (first 6 or fewer are printed) #> [,1] [,2] #> [1,] 1.267221 0.7722282 #> [2,] 1.265509 0.7442478 #> [3,] 1.267221 0.7722282 #> [4,] 1.482080 1.1991317 #> [5,] 1.482080 1.1991317 #> [6,] 1.265509 0.7442478 #> #> Parameters of the digraph #> $center #> [1] 1.6 1.2 #> #> Various quantities of the digraph #> number of vertices number of partition points #> 5.0 3.0 #> number of triangles number of arcs #> 1.0 10.0 #> arc density #> 0.5 plot(Arcs)
To see the code and the plot for the arcs using the plot.PCDs function,
with vertex names and annotation added to the plot,
type ? arcsAStri.
Functions for Proportional Edge PCDs with Vertices in One Triangle {#sec:PE-PCD-one-tri}
In this section, we use the same triangle $T$ and generate data as in Section \@ref(sec:AS-PCD-one-tri).
#A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); n<-5 #set.seed(1); Xp<-runif.tri(n,Tr)$g M<-c(1.6,1.0) #try also M<-as.numeric(runif.tri(1,Tr)$g) r<-1.5 #try also r<-2
And choose the expansion parameter $r=$ r r to illustrate proportional edge (PE)
proximity regions and the associated PCDs.
The function NPEtri is used for the construction of PE proximity regions taking the arguments
p,tri,r,M=c(1,1,1),rv=NULL
where
p,tri,rv=NULLare as in the functionNAStri, andMis the center to construct the vertex regions with defaultM=c(1,1,1)(i.e. the center of mass),rvthe index of the vertex regions that the pointpresides in with defaultrv=NULLandris the expansion parameter (must be $\ge 1$).
NPEtri returns the PE proximity region (i.e., the vertices of the triangular proximity region).
P<-c(1.8,.5) NPEtri(P,Tr,r,M) #> [,1] [,2] #> [1,] 2.000 0.00 #> [2,] 1.550 0.45 #> [3,] 1.775 0.90
Indicator for the presence of an arc from a (data or $\X$) point to another for PE-PCDs is the function IarcPEtri.
One can use it for points in the data set or for arbitrary points (as if they were in the data set).
It takes the arguments, p1,p2,tri,r,M=c(1,1,1),rv=NULL where
-p1,p2,tri,rv=NULL are as in IarcAStri and
- r,M=c(1,1,1) are as in NPEtri.
This function returns $I(p2 \in N_{PE}(p1))$,
that is, returns 1 if p2 is in $N_{PE}(p1)$, 0 otherwise.
P1<-as.numeric(runif.tri(1,Tr)$g) P2<-as.numeric(runif.tri(1,Tr)$g) IarcPEtri(P1,P2,Tr,r,M) #> [1] 1 IarcPEtri(Xp[1,],Xp[5,],Tr,r,M) #try also IarcPEtri(Xp[5,],Xp[1,],Tr,r,M) #> [1] 0
Number of arcs of the PE-PCD can be computed by the function num.arcsPEtri,
which is an object of class "NumArcs" and takes arguments Xp,tri,r,M=c(1,1,1)
where Xp is the data set, and the others are as above.
The output is as in num.arcsAStri.
Narcs = num.arcsPEtri(Xp,Tr,r,M) summary(Narcs) #> Call: #> num.arcsPEtri(Xp = Xp, tri = Tr, r = r, M = M) #> #> Description of the output: #> Number of Arcs of the PE-PCD with vertices Xp and Quantities Related to the Support Triangle #> #> Number of data (Xp) points in the triangle = 5 #> Number of arcs in the digraph = 7 #> #> Indices of data points in the triangle: #> 1 2 3 4 5 #> #plot(Narcs)
The arc density of the PE-PCD can be computed by the function PEarc.dens.tri.
It takes the arguments Xp,tri,r,M,tri.cor where Xp is the data set (or $\X$ points)
r,tri,M are as above
and returns output as list with elements
arc.dens, arc density of PE-PCD whose vertices are the 2D numerical data set,Xp,std.arc.dens, the arc density standardized by the mean and asymptotic variance of the arc density of PE-PCD for uniform data in the triangletri, andcaveata warning as"The standardized arc density is only correct when M is the center of mass in the current version".
Arc density can be adjusted (or corrected) for the points outside the triangle by using the option tri.cor=TRUE as in the ASarc.dens.tri.
Moreover, the standardized arc density is only correct when $M$ is the center of mass in the current version.
PEarc.dens.tri(Xp,Tr,r,M) #> $arc.dens #> [1] 0.35
The incidence matrix of the PE-PCD for the one triangle case can be found by the function inci.matPEtri, using e.g. the inci.matPEtri(Xp,Tr,r,M) command.
It takes the same arguments as the function num.arcsPEtri.
Plot of the arcs in the digraph PE-PCD, which is based on the $M$-vertex regions with $M=$(r M[1],r M[2]),
can be obtained by the function plotPEarcs.tri, which is the one-tri counterpart of the function plotPEarcs.
It takes arguments Xp,tri,r,M=c(1,1,1) (as in num.arcsPEtri) and other options for the plot function.
See the help page for the function using ? plotPEarcs.tri.
Plot of the arcs of the above PE-PCD, together with the vertex regions
(with the option vert.reg = TRUE)
and vertex names added to the figure.
plotPEarcs.tri(Xp,Tr,r,M,xlab="",ylab="",vert.reg = TRUE) #add vertex labels and text to the figure (with vertex regions) ifelse(isTRUE(all.equal(M,circumcenter.tri(Tr))), {Ds<-rbind((B+C)/2,(A+C)/2,(A+B)/2); cent.name="CC"},{Ds<-prj.cent2edges(Tr,M); cent.name="M"}) txt<-rbind(Tr,M,Ds) xc<-txt[,1]+c(-.02,.02,.02,.02,.04,-0.03,-.01) yc<-txt[,2]+c(.02,.02,.02,.05,.02,.04,-.06) txt.str<-c("A","B","C",cent.name,"D1","D2","D3") text(xc,yc,txt.str)
Plots of the PE proximity regions can be obtained by the function plotPEregs.tri.
It takes arguments Xp,tri,r,M=c(1,1,1) (as in num.arcsPEtri) and other options for the plot function.
See the help page for the function using ? plotPEregs.tri.
M<-c(1.6,1.2) #try also M<-c(1.6620051,0.8136604) or M="CC" plotPEregs.tri(Xp,Tr,r,M,vert.reg = T,xlab="",ylab="")
The function ArcsPEtri is an object of class "PCDs".
It takes arguments Xp,tri,r,M=c(1,1,1) (as in num.arcsPEtri).
The output list is as in the arcsAStri
except the parameters
of the digraph (center for PE-PCD and the expansion parameter).
The plot function returns the same plot as in plotPEarcs.tri,
hence we comment it out below.
Arcs<-ArcsPEtri(Xp,Tr,r,M) #or try with the default center Arcs<-ArcsPEtri(Xp,Tr,r); M= (Arcs$param)$cent Arcs #> Call: #> ArcsPEtri(Xp = Xp, tri = Tr, r = r, M = M) #> #> Type: #> [1] "Proportional Edge Proximity Catch Digraph (PE-PCD) for 2D Points in the Triangle with Expansion Parameter r = 1.5 and Center M = (1.6,1.2)" summary(Arcs) #> Call: #> ArcsPEtri(Xp = Xp, tri = Tr, r = r, M = M) #> #> Type of the digraph: #> [1] "Proportional Edge Proximity Catch Digraph (PE-PCD) for 2D Points in the Triangle with Expansion Parameter r = 1.5 and Center M = (1.6,1.2)" #> #> Vertices of the digraph = Xp #> Partition points of the region = Tr #> #> Selected tail (or source) points of the arcs in the digraph #> (first 6 or fewer are printed) #> [,1] [,2] #> [1,] 1.265509 0.7442478 #> [2,] 1.380035 1.5548904 #> [3,] 1.380035 1.5548904 #> [4,] 1.380035 1.5548904 #> [5,] 1.380035 1.5548904 #> [6,] 1.267221 0.7722282 #> #> Selected head (or end) points of the arcs in the digraph #> (first 6 or fewer are printed) #> [,1] [,2] #> [1,] 1.267221 0.7722282 #> [2,] 1.265509 0.7442478 #> [3,] 1.687023 0.7682074 #> [4,] 1.267221 0.7722282 #> [5,] 1.482080 1.1991317 #> [6,] 1.265509 0.7442478 #> #> Parameters of the digraph #> $center #> [1] 1.6 1.2 #> #> $`expansion parameter` #> [1] 1.5 #> #> Various quantities of the digraph #> number of vertices number of partition points #> 5.0 3.0 #> number of triangles number of arcs #> 1.0 10.0 #> arc density #> 0.5 plot(Arcs)
To see the code and the plot for the arcs using the plot.PCDs function,
with vertex names and annotation added to the plot,
type ? ArcsPEtri.
Functions for Central Similarity PCDs with Vertices in One Triangle {#sec:CS-PCD-one-tri}
We use the same triangle $T$ and generated data as Section \@ref(sec:PE-PCD-one-tri).
tau<-1.5
And choose the expansion parameter $\tau=$ r tau
to illustrate central similarity proximity regions and the associated PCDs.
The function NCStri is used for the construction of CS proximity regions taking the arguments
p,tri,t,M=c(1,1,1),re=NULL where
p,tri,M=c(1,1,1)are as in the functionNPEtri, andrethe index of the edge regions that the pointpresides in with defaultre=NULLandtis the expansion parameter (must be $> 0$).
NCStri returns the CS proximity region (i.e., the vertices of the triangular proximity region).
P<-c(1.8,.5) NCStri(P,Tr,tau,M) #> [,1] [,2] #> [1,] 1.74375 0.9500 #> [2,] 1.51250 0.4875 #> [3,] 1.97500 0.0250
Indicator for the presence of an arc from a (data or $\X$) point to another for CS-PCDs is the function IarcCStri.
One can use it for points in the data set or for arbitrary points (as if they were in the data set).
It takes the arguments, p1,p2,tri,t,M,re=NULL where p1,p2,tri,M are as in IarcPEtri
and t,re=NULL are as in NCStri.
This function returns $I(p2 \in N_{CS}(p1))$, that is, returns 1 if p2 is in $N_{CS}(p1)$, 0 otherwise.
P1<-as.numeric(runif.tri(1,Tr)$g) P2<-as.numeric(runif.tri(1,Tr)$g) IarcCStri(P1,P2,Tr,tau,M) #> [1] 1 IarcCStri(Xp[1,],Xp[2,],Tr,tau,M) #> [1] 0
Number of arcs of the CS-PCD can be computed by the function num.arcsCStri.
The output is as in num.arcsPEtri.
Number of arcs of the CS-PCD can be computed by the function num.arcsCStri,
which is an object of class "NumArcs" and takes arguments Xp,tri,t,M=c(1,1,1)
where Xp is the data set, and the others are as above.
The output is as in num.arcsPEtri.
Narcs = num.arcsCStri(Xp,Tr,t=.5,M) summary(Narcs) #> Call: #> num.arcsCStri(Xp = Xp, tri = Tr, t = 0.5, M = M) #> #> Description of the output: #> Number of Arcs of the CS-PCD with vertices Xp and Quantities Related to the Support Triangle #> #> Number of data (Xp) points in the triangle = 5 #> Number of arcs in the digraph = 0 #> #> Indices of data points in the triangle: #> 1 2 3 4 5 #> #plot(Narcs)
The arc density of the CS-PCD can be computed by the function CSarc.dens.tri.
It takes the arguments Xp,tri,t,M=c(1,1,1),tri.cor=FALSE where Xp is the data set (or $\X$ points)
tri,t,M=c(1,1,1),tri.cor=FALSE are as above.
Arc density can be adjusted (or corrected) for the points outside the triangle by using the option tri.cor=TRUE as in the num.arcsPEtri.
CSarc.dens.tri(Xp,Tr,tau,M) #> $arc.dens #> [1] 0.35
The incidence matrix of the CS-PCD for the one triangle case can be found by inci.matCStri, using the inci.matCStri(Xp,Tr,tau,M) command.
It takes the same arguments as the function num.arcsCStri.
Plot of the arcs in the digraph CS-PCD, which is based on the $M$-edge regions with $M=$(r M[1],r M[2]).
can be obtained by the function plotCSarcs.tri, which is the one-tri counterpart of the function plotCSarcs.
It takes arguments Xp,tri,t,M=c(1,1,1) (as in num.arcsPEtri) and other options for the plot function.
See the help page for the function using ? plotCSarcs.tri.
Plot of the arcs of the above CS-PCD, together with the edge regions
(with the option edge.reg = TRUE)
and vertex names added to the figure.
t<-1.5 #try also t<-2 plotCSarcs.tri(Xp,Tr,t,M,xlab="",ylab="",edge.reg = TRUE) txt<-rbind(Tr,M) xc<-txt[,1]+c(-.02,.02,.02,.03) yc<-txt[,2]+c(.02,.02,.02,.03) txt.str<-c("A","B","C","M") text(xc,yc,txt.str)
Plot of the CS proximity regions can be obtained by the function plotCSregs.tri,
the first is for all points, the second is for two $\X$ points only (for better visualization).
It takes arguments Xp,tri,t,M=c(1,1,1) (as in num.arcsCStri) and other options for the plot function.
See the help page for the function using ? plotCSregs.tri.
plotCSregs.tri(Xp,Tr,t,M,edge.reg=T,xlab="",ylab="")
The function arcsCStri is an object of class "PCDs".
It takes arguments Xp,tri,t,M=c(1,1,1) (as in num.arcsCStri).
The output list is as in the ArcsPEtri.
The plot function returns the same plot as in plotCSarcs.tri,
hence we comment it out below.
Arcs<-arcsCStri(Xp,Tr,t,M) Arcs #> Call: #> arcsCStri(Xp = Xp, tri = Tr, t = t, M = M) #> #> Type: #> [1] "Central Similarity Proximity Catch Digraph (CS-PCD) for 2D Points in the Triangle with Expansion Parameter t = 1.5 and Center M = (1.6,1.2)" summary(Arcs) #> Call: #> arcsCStri(Xp = Xp, tri = Tr, t = t, M = M) #> #> Type of the digraph: #> [1] "Central Similarity Proximity Catch Digraph (CS-PCD) for 2D Points in the Triangle with Expansion Parameter t = 1.5 and Center M = (1.6,1.2)" #> #> Vertices of the digraph = Xp #> Partition points of the region = Tr #> #> Selected tail (or source) points of the arcs in the digraph #> (first 6 or fewer are printed) #> [,1] [,2] #> [1,] 1.687023 0.7682074 #> [2,] 1.687023 0.7682074 #> [3,] 1.687023 0.7682074 #> [4,] 1.267221 0.7722282 #> [5,] 1.482080 1.1991317 #> [6,] 1.482080 1.1991317 #> #> Selected head (or end) points of the arcs in the digraph #> (first 6 or fewer are printed) #> [,1] [,2] #> [1,] 1.265509 0.7442478 #> [2,] 1.267221 0.7722282 #> [3,] 1.482080 1.1991317 #> [4,] 1.265509 0.7442478 #> [5,] 1.265509 0.7442478 #> [6,] 1.687023 0.7682074 #> #> Parameters of the digraph #> $center #> [1] 1.6 1.2 #> #> $`expansion parameter` #> [1] 1.5 #> #> Various quantities of the digraph #> number of vertices number of partition points #> 5.0 3.0 #> number of triangles number of arcs #> 1.0 8.0 #> arc density #> 0.4 plot(Arcs)
To see the code and the plot for the arcs using the plot.PCDs function,
with vertex names and annotation added to the plot,
type ? arcsCStri.
Functions for Proportional Edge PCDs with Vertices in the Standard Equilateral Triangle {#sec:PE-PCD-inTe}
We first define the standard equilateral triangle $T_e=T(A,B,C)$ with vertices $A=(0,0)$, $B=(1,0)$, and $C=(1/2,\sqrt{3}/2)$.
A<-c(0,0); B<-c(1,0); C<-c(1/2,sqrt(3)/2); Te<-rbind(A,B,C); n<-5 #try also n<-10, 50, or 100 set.seed(1) Xp<-runif.std.tri(n)$gen.points M<-c(.6,.2) #try also M<-c(1,1,1)
Then we generate data or $\X$ points of size $n=$ r n using the function runif.std.tri in the pcds package,
and choose the arbitrary center $M=($ r M[1],r M[2] $)$ in the interior of $T_e$.
Notice that the argument tri is redundant for the functions specific to the standard equilateral triangle,
hence they are omitted for the functions which have counterparts in Section \@ref(sec:PE-PCD-one-tri).
Plot of the triangle $T_e$ and the $\X$ points in it can be obtained by the below code,
and we also add the vertex names and
annotation using the text function from base R.
We use asp=1 in the below plot so that the standard equilateral triangle is plotted with equal length edges.
Xlim<-range(Te[,1]) Ylim<-range(Te[,2]) plot(Te,asp=1,pch=".",xlab="",ylab="",xlim=Xlim,ylim=Ylim,main="Points in Standard Equilateral Triangle") polygon(Te) points(Xp) #add the vertex names and annotation txt<-rbind(Te) xc<-txt[,1]+c(-.02,.02,.02) yc<-txt[,2]+c(.01,.01,.01) txt.str<-c("A","B","C") text(xc,yc,txt.str)
Alternatively, we can use the plotDelaunay.tri function in pcds to obtain the same plot by executing plotDelaunay.tri(Xp,Te,xlab="x",ylab="y",main="Points in Standard Equilateral Triangle") command.
Indicator for the presence of an arc from a (data or $\X$) point to another for PE-PCDs is the function IarcPEstd.tri.
One can use it for points in the data set or for arbitrary points (as if they were in the data set).
It takes the arguments, p1,p2,r,M=c(1,1,1),rv=NULL as in IarcPEtri.
P1<-as.numeric(runif.tri(1,Te)$g) P2<-as.numeric(runif.tri(1,Te)$g) r=2 IarcPEstd.tri(P1,P2,r,M) #> [1] 1 IarcPEstd.tri(Xp[1,],Xp[2,],r,M) #> [1] 1
Number of arcs of the PE-PCD can be computed by the function num.arcsPEstd.triwhich
which is an object of class "NumArcs" and takes arguments as the function num.arcsPEtri.
The output is as in num.arcsPEtri.
Narcs = num.arcsPEstd.tri(Xp,r=1.25,M) summary(Narcs) #> Call: #> num.arcsPEstd.tri(Xp = Xp, r = 1.25, M = M) #> #> Description of the output: #> Number of Arcs of the PE-PCD and the Related Quantities with vertices Xp in the Standard Equilateral Triangle #> #> Number of data (Xp) points in the triangle = 5 #> Number of arcs in the digraph = 5 #> #> Indices of data points in the triangle: #> 1 2 3 4 5 #> #plot(Narcs)
The incidence matrix of the PE-PCD for the one triangle case can be found by inci.matPETe,
using the inci.matPETe(Xp,r,M) command.
Functions for Central Similarity PCDs with Vertices in the Standard Equilateral Triangle
We use the same setting for the data points and the center as in Section \@ref(sec:PE-PCD-inTe).
Notice that the argument tri is redundant for the functions specific to the standard equilateral triangle,
hence they are omitted for the functions which have counterparts in Section \@ref(sec:CS-PCD-one-tri).
Indicator for the presence of an arc from a (data or $\X$) point to another for CS-PCDs is the function IarcPEstd.tri.
P1<-as.numeric(runif.tri(1,Te)$g) P2<-as.numeric(runif.tri(1,Te)$g) tau=1 IarcCSstd.tri(P1,P1,tau,M) IarcCSstd.tri(P1,P2,tau,M) IarcCSstd.tri(Xp[1,],Xp[2,],tau,M)
Number of arcs of the CS-PCD can be computed by the function num.arcsCSstd.tri
which is an object of class "NumArcs" and its
arguments and output are as in num.arcsCStri.
set.seed(123) M<-as.numeric(runif.std.tri(1)$g) #try also M<-c(.6,.2) Narcs = num.arcsCStri(Xp,Te,t=1.5,M) summary(Narcs) #> Call: #> num.arcsCStri(Xp = Xp, tri = Te, t = 1.5, M = M) #> #> Description of the output: #> Number of Arcs of the CS-PCD with vertices Xp and Quantities Related to the Support Triangle #> #> Number of data (Xp) points in the triangle = 5 #> Number of arcs in the digraph = 3 #> #> Indices of data points in the triangle: #> 1 2 3 4 5 #> #plot(Narcs)
The incidence matrix of the CS-PCD for the one triangle case can be found by inci.matCSstd.tri,
using e.g. the inci.matCSstd.tri(Xp,t=1.5,M) command.
Auxiliary Functions to Define Proximity Regions for Points in a Triangle
The PCDs are constructed using proximity regions, and proximity region of an $\X$ point depends on its location relative to the $\Y$ points. In particular, AS- and PE-PCDs depend on the vertex regions and CS-PCDs depend on edge regions. We will illustrate the vertex and edge regions in this section. These regions partition the Delaunay cell (e.g., Delaunay triangle in $\mathbb R^2$) based on a center or central point in the triangle.
We first check whether a point is inside a triangle or not which can be done using the function in.triangle,
which takes arguments p,tri,boundary=FALSE where
p, a 2D point to be checked whether it is inside the triangle or not,triA $3 \times 2$ matrix with each row representing a vertex of the triangle,boundary, a logical parameter (default=TRUE) to include boundary or not, so if it isTRUE, the function checks if the point,p, lies in the closure of the triangle (i.e., interior and boundary combined); else, it checks ifplies in the interior of the triangle.
The function gives TRUE if the point is inside the triangle, and FALSE otherwise.
It also returns the barycentric coordinates of the point with respect to the triangle.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); p<-c(1.4,1.2) Tr<-rbind(A,B,C) in.triangle(p,Tr) #> $in.tri #> [1] TRUE #> #> $barycentric #> [1] 0.4 0.2 0.4
We now illustrate circumcenter of a triangle.
The function circumcenter.tri takes tri as its sole argument for a triangle and
returns the circumcenter of the triangle as its output.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); #the vertices of the triangle Tr (CC<-circumcenter.tri(Tr)) #the circumcenter #> [1] 2.083333 1.083333
We plot the circumcenter of an obtuse triangle below (so circumcenter is outside of the triangle)
using the below code,
see also the help page with ? circumcenter.tri for the code to generate this figure.
Notice that in the code we used asp=1 so that lines joining CC to the edges of the triangle
appear perpendicular to the edges.
D1<-(B+C)/2; D2<-(A+C)/2; D3<-(A+B)/2; #midpoints of the edges Ds<-rbind(D1,D2,D3) Xlim<-range(Tr[,1],CC[1]) Ylim<-range(Tr[,2],CC[2]) xd<-Xlim[2]-Xlim[1] yd<-Ylim[2]-Ylim[1] par(pty="s") plot(A,asp=1,pch=".",xlab="",ylab="", main="Circumcenter of a Triangle", axes=TRUE,xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05)) polygon(Tr) points(rbind(CC)) L<-matrix(rep(CC,3),ncol=2,byrow=TRUE); R<-Ds segments(L[,1], L[,2], R[,1], R[,2], lty=2) txt<-rbind(Tr,CC,Ds) xc<-txt[,1]+c(-.08,.08,.08,.12,-.09,-.1,-.09) yc<-txt[,2]+c(.02,-.02,.03,-.06,.02,.06,-.04) txt.str<-c("A","B","C","CC","D1","D2","D3") text(xc,yc,txt.str)
The function circumcenter.basic.tri is a special case of circumcenter.tri and takes the argument c1,c2
and returns the circumcenter of the standard basic triangle.
The function center.nondegPE takes arguments tri,r
which are the triangle and the expansion parameter for the PE proximity regions, respectively.
It returns the three centers for non-degenerate asymptotic distribution of
domination number of PE-PCDs for $r \in (1,1.5)$ and the center of mass for $r=1.5$.
This center is not defined for $r > 1.5$, as the asymptotic distribution is degenerate in this case.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); r<-1.35 (Ms<-center.nondegPE(Tr,r)) #> [,1] [,2] #> M1 1.388889 1.0000000 #> M2 1.611111 0.7777778 #> M3 1.500000 1.2222222
We plot the non-degeneracy centers for the triangle Tr using the below code,
type also ? center.nondegPE for the code to generate this figure.
Xlim<-range(Tr[,1]) Ylim<-range(Tr[,2]) xd<-Xlim[2]-Xlim[1] yd<-Ylim[2]-Ylim[1] plot(Tr,pch=".",xlab="",ylab="", main="Centers of nondegeneracy of the domination number\n of the PE-PCD in a triangle", axes=TRUE,xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05)) polygon(Tr) points(Ms,pch=".",col=1) polygon(Ms,lty=2) xc<-Tr[,1]+c(-.02,.02,.02) yc<-Tr[,2]+c(.02,.02,.03) txt.str<-c("A","B","C") text(xc,yc,txt.str) xc<-Ms[,1]+c(-.04,.04,.03) yc<-Ms[,2]+c(.02,.02,.05) txt.str<-c(expression(M[1]),"M2","M3") text(xc,yc,txt.str)
The function prj.cent2edges returns the projections of a point (e.g., a center) $M$ inside a triangle to its edges,
i.e., it returns the intersection point where the line joining a vertex to $M$ crosses the opposite edge.
The line segments between $M$ the intersection points define the $M$-vertex regions.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); M<-c(1.6,1.0) #try also M<-as.numeric(runif.tri(1,Tr)$g) (Ds<-prj.cent2edges(Tr,M)) #try also prj.cent2edges(Tr,M=c(1,1)) #> [,1] [,2] #> [1,] 1.750000 1.0000000 #> [2,] 1.333333 1.6666667 #> [3,] 1.666667 0.3333333
We plot the projections of center $M$ to the edges of the triangle Tr using the below code,
type also ? prj.cent2edges for the code to generate this figure.
M<-c(1.6,1.0) Xlim<-range(Tr[,1]) Ylim<-range(Tr[,2]) xd<-Xlim[2]-Xlim[1] yd<-Ylim[2]-Ylim[1] if (dimension(M)==3) {M<-bary2cart(M,Tr)} #need to run this when M is given in barycentric coordinates plot(Tr,pch=".",xlab="",ylab="", main="Projection of Center M to the edges of a triangle",axes=TRUE, xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05)) polygon(Tr) L<-rbind(M,M,M); R<-Ds segments(L[,1], L[,2], R[,1], R[,2], lty=2) xc<-Tr[,1] yc<-Tr[,2] txt.str<-c("rv=1","rv=2","rv=3") text(xc,yc,txt.str) txt<-rbind(M,Ds) xc<-txt[,1]+c(-.02,.04,-.04,-.02) yc<-txt[,2]+c(-.02,.04,.04,-.06) txt.str<-c("M","D1","D2","D3") text(xc,yc,txt.str)
The function prj.cent2edges.basic.tri is a special case of prj.cent2edges
taking arguments c1,c2 instead of tri
and returning projections of $M$ to the edges in the standard basic triangle.
The function prj.nondegPEcent2edges takes arguments
tri, a $3 \times 2$ matrix with each row representing a vertex of the triangle.r, a positive real number which serves as the expansion parameter in PE proximity region, must be in $(1,1.5]$ for this function.cent, an index of the center (as $1,2,3$ corresponding to $M_1,\,M_2,\,M_3$) which gives nondegenerate asymptotic distribution of the domination number of PE-PCD for uniform data intrifor expansion parameterrin $(1,1.5]$; defaultcent=1.
This function returns the projections of nondegeneracy centers (i.e. centers for non-degenerate asymptotic distribution of domination number of PE-PCDs) to its edges, i.e., it returns the intersection point where the line joining a vertex to a non-degeneracy center $M_i$ crosses the opposite edge. The line segments between $M_i$ the intersection points will provide the $M_i$-vertex regions.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); r<-1.35 prj.nondegPEcent2edges(Tr,r,cent=2) #> [,1] [,2] #> [1,] 1.825 0.70 #> [2,] 1.250 1.50 #> [3,] 1.650 0.35
We plot the projections of the non-degeneracy center $M_1$ to the edges of a triangle using the below code,
type also ? prj.nondegPEcent2edges for the code to generate this figure.
Ms<-center.nondegPE(Tr,r) M1=Ms[1,] Ds<-prj.nondegPEcent2edges(Tr,r,cent=1) Xlim<-range(Tr[,1]) Ylim<-range(Tr[,2]) xd<-Xlim[2]-Xlim[1] yd<-Ylim[2]-Ylim[1] plot(Tr,pch=".",xlab="",ylab="", main="Projections from a non-degeneracy center for domination number\n of PE-PCD to the edges of the triangle", axes=TRUE,xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05)) polygon(Tr) points(Ms,pch=".",col=1) polygon(Ms,lty=2) xc<-Tr[,1]+c(-.02,.03,.02) yc<-Tr[,2]+c(-.02,.04,.04) txt.str<-c("A","B","C") text(xc,yc,txt.str) txt<-Ms xc<-txt[,1]+c(-.02,.04,-.04) yc<-txt[,2]+c(-.02,.04,.04) txt.str<-c("M1","M2","M3") text(xc,yc,txt.str) points(Ds,pch=4,col=2) L<-rbind(M1,M1,M1); R<-Ds segments(L[,1], L[,2], R[,1], R[,2], lty=2,lwd=2,col=4) txt<-Ds xc<-txt[,1]+c(-.02,.04,-.04) yc<-txt[,2]+c(-.02,.04,.04) txt.str<-c("D1","D2","D3") text(xc,yc,txt.str)
The function in.triangle takes arguments
p, a 2D point to be checked whether it is inside the triangle or not,tri, a $3 \times 2$ matrix with each row representing a vertex of the triangle,boundary, a logical parameter (default=FALSE) to include boundary or not, so if it isTRUE, the function checks if the point,p, lies in the closure of the triangle (i.e., interior and boundary combined) else it checks ifplies in the interior of the triangle.
This function can be used to check whether a point p is inside a triangle tri or not.
On the other hand in.tri.all takes arguments Xp,tri,boundary where
Xp is a 2D data set and tri,boundary are as in the function in.triangle.
This function checks whether all of the points in a data set are inside the triangle or not.
We check in.tri.all with $n=5$ data points generated uniformly in the unit square and in the triangle.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); p<-c(1.4,1.2) Tr<-rbind(A,B,C) in.triangle(p,Tr) #> $in.tri #> [1] TRUE #> #> $barycentric #> [1] 0.4 0.2 0.4 #data set and checking all points in it are inside the triangle or not n<-5 Xp1<-cbind(runif(n),runif(n)) in.tri.all(Xp1,Tr) #> [1] FALSE
The function is.std.eq.tri takes the argument tri (a $3 \times 2$ matrix for a triangle) and
can be used for checking the triangle tri is standard equilateral triangle or not,
regardless of the order of the vertices.
A<-c(0,0); B<-c(1,0); C<-c(1/2,sqrt(3)/2); Te<-rbind(A,B,C) #try adding +10^(-16) to each vertex is.std.eq.tri(Te) #> [1] TRUE A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); is.std.eq.tri(Tr) #> [1] FALSE
The function as.basic.tri takes the arguments tri,scaled where
-tri is as in is.std.eq.tri and
-scaled is a logical argument for scaling the resulting triangle. If scaled=TRUE, then the resulting triangle is
scaled to be a regular basic triangle, i.e., longest edge having unit length,
else the new triangle $T(A,B,C)$ is nonscaled. The default is scaled=FALSE.
It converts any triangle to a basic triangle (up to translation and rotation),
so that the output triangle is $T(A',B',C')$ so that edges in decreasing length are $A'B'$, $B'C'$, and $A'C'$.
The option scaled scales the output triangle when scaled=TRUE so that longest edge $A'B'$ has unit length
(default is scaled=FALSE).
Most of the times,
the resulting triangle will still need to be translated and/or rotated to be in the standard basic triangle form.
c1<-.4; c2<-.6 A<-c(0,0); B<-c(1,0); C<-c(c1,c2); as.basic.tri(rbind(B,C,A)) #> $tri #> [,1] [,2] #> A 0.0 0.0 #> B 1.0 0.0 #> C 0.4 0.6 #> #> $desc #> [1] "Edges (in decreasing length are) AB, BC, and AC" #> #> $orig.order #> [1] 3 1 2 x<-c(1,1); y<-c(2,0); z<-c(1.5,2); as.basic.tri(rbind(x,y,z)) #> $tri #> [,1] [,2] #> A 1.5 2 #> B 2.0 0 #> C 1.0 1 #> #> $desc #> [1] "Edges (in decreasing length are) AB, BC, and AC" #> #> $orig.order #> [1] 3 2 1 as.basic.tri(rbind(x,y,z),scaled = TRUE) #> $tri #> [,1] [,2] #> A 0.7276069 0.9701425 #> B 0.9701425 0.0000000 #> C 0.4850713 0.4850713 #> #> $desc #> [1] "Edges (in decreasing length are) AB, BC, and AC" #> #> $orig.order #> [1] 3 2 1
The function tri2std.basic.tri converts a triangle to the standard basic triangle form,
and its output only returns $c_1,c_2$ in $Cvec and
also the original order of the vertices in the input triangle
by $orig.order.
c1<-.4; c2<-.6 A<-c(0,0); B<-c(1,0); C<-c(c1,c2); tri2std.basic.tri(rbind(B,C,A)) #> $Cvec #> [1] 0.4 0.6 #> #> $orig.order #> [1] 3 1 2 A<-c(1,1); B<-c(2,0); C<-c(1.5,2); tri2std.basic.tri(rbind(A,B,C)) #> $Cvec #> [1] 0.4117647 0.3529412 #> #> $orig.order #> [1] 3 2 1
Barycentric coordinates are very useful in defining and analyzing the triangular (and simplicial) proximity regions.
So, we provide functions to convert barycentric coordinates to Cartesian coordinates (bary2cart) and vice versa (cart2bary).
Both functions take the arguments P,tri where P is th point to change the coordinates for and tri is the reference triangle
(as a $3\times2$ matrix).
As the names suggest,
the function cart2bary converts the point P in Cartesian coordinates
to barycentric coordinates with respect to the triangle tri,
and bary2cart converts the point P in barycentric coordinates with respect to the triangle tri to Cartesian coordinates.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); cart2bary(c(1.4,1.2),Tr) #> [1] 0.4 0.2 0.4 bary2cart(c(1.4,1.2,1),Tr) #> [1] 1.4722222 0.9444444 CM<-(A+B+C)/3; CM #> [1] 1.5 1.0 cart2bary(CM,Tr) #> [1] 0.3333333 0.3333333 0.3333333 bary2cart(c(1,1,1),Tr) #> [1] 1.5 1.0
The function index.delaunay.tri takes the arguments
p, a 2D point; the index of the Delaunay triangle in whichpresides is to be determined. It is an argument forindex.delaunay.tri.Yp, a set of 2D points from which Delaunay triangulation is constructed,DTmesh, Delaunay triangles based onYp, default isNULL, which is computed viainterp::tri.meshfunction ininterppackage.interp::trianglesfunction returns a triangulation data structure from the triangulation object created bytri.mesh.
This function returns the index of the Delaunay triangle in which the given point resides.
Similarly, the function indices.delaunay.tri takes the arguments Xp,Yp,DTmesh where
Xp is the set of data points for which the indices of the Delaunay
triangles they reside is to be determined and Yp,DTmesh are as in index.delaunay.tri.
This function returns the indices of triangles for all the points in a data set as a vector.
nx<-10 #number of X points (target) ny<-5 #number of Y points (nontarget)
We generate $n_x=$ r nx $\X$ points uniformly in the convex hull of $n_y=$ r ny $\Y$ points
using the function runif.multi.tri in pcds package.
set.seed(1) Yp<-cbind(runif(ny),runif(ny)) Xp<-runif.multi.tri(nx,Yp)$g #data under CSR in the convex hull of Ypoints #try also Xp<-cbind(runif(nx),runif(nx)) index.delaunay.tri(Xp[10,],Yp) #> [1] 2 #or use DTY<-interp::tri.mesh(Yp[,1],Yp[,2],duplicate="remove") #Delaunay triangulation index.delaunay.tri(Xp[10,],Yp,DTY) #> [1] 2 (tr.ind<-indices.delaunay.tri(Xp,Yp,DTY)) #indices of the Delaunay triangles #> [1] 3 3 1 4 3 2 3 3 2 2
We provide the scatterplot of $\X$ points (labeled according to the Delaunay triangle they reside in),
and the Delaunay triangulation
of $n_y=$ r ny $\Y$ points using the below code. Type also ? indices.delaunay.tri.
Xlim<-range(Yp[,1],Xp[,1]) Ylim<-range(Yp[,2],Xp[,2]) xd<-Xlim[2]-Xlim[1] yd<-Ylim[2]-Ylim[1] # plot of the data in the convex hull of Y points together with the Delaunay triangulation #par(pty="s") plot(Xp,main="X Points in Delaunay Triangles for Y Points", xlab=" ", ylab=" ", xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05),pch=".") interp::plot.triSht(DTY, add=TRUE, do.points = TRUE,pch=16,col="blue") text(Xp,labels = factor(tr.ind) )
Functions Related to the Vertex Regions
The function rel.vert.tri takes arguments p,tri,M
where p is a 2D point for which M-vertex region it resides in is to be determined in the
triangle tri and tri,M are for the triangle and the center used to construct the vertex regions, respectively.
This function returns rv, the index (i.e., the row number in tri) of the vertex of tri whose region contains the point p
and the vertices of the tri.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); M<-c(1.6,1.0) P<-c(1.8,.5) rel.vert.tri(P,Tr,M) #> $rv #> [1] 2 #> #> $tri #> [,1] [,2] #> vertex 1 1.0 1 #> vertex 2 2.0 0 #> vertex 3 1.5 2
n<-5 #try also n<-10, 50, or 100
We illustrate the vertex regions using the following code.
We annotate the vertices with corresponding indices with rv=i for $i=1,2,3$,
and also generate $n=$ r n points inside the triangle and
provide the scatterplot of these points labeled according to the vertex region
they reside in.
Type also ? rel.vert.tri for the code to generate this figure.
set.seed(1) Xp<-runif.tri(n,Tr)$g M<-c(1.6,1.0) #try also M<-as.numeric(runif.tri(1,Tr)$g) Rv<-vector() for (i in 1:n) {Rv<-c(Rv,rel.vert.tri(Xp[i,],Tr,M)$rv)} Rv Ds<-prj.cent2edges(Tr,M) Xlim<-range(Tr[,1],Xp[,1]) Ylim<-range(Tr[,2],Xp[,2]) xd<-Xlim[2]-Xlim[1] yd<-Ylim[2]-Ylim[1] if (dimension(M)==3) {M<-bary2cart(M,Tr)} #need to run this when M is given in barycentric coordinates plot(Tr,pch=".",xlab="",ylab="",main="Illustration of M-Vertex Regions\n in a Triangle",axes=TRUE, xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05)) polygon(Tr) points(Xp,pch=".",col=1) L<-rbind(M,M,M); R<-Ds segments(L[,1], L[,2], R[,1], R[,2], lty=2) xc<-Tr[,1] yc<-Tr[,2] txt.str<-c("rv=1","rv=2","rv=3") text(xc,yc,txt.str) txt<-rbind(M,Ds) xc<-txt[,1]+c(-.02,.04,-.04,0) yc<-txt[,2]+c(-.02,.04,.05,-.08) txt.str<-c("M","D1","D2","D3") text(xc,yc,txt.str) text(Xp,labels=factor(Rv))
The following functions are special cases of rel.vert.tri, where
rel.vert.basic.tritakesc1,c2fortriand returns the same output for the standard basic triangle,rel.vert.basic.triCMtakes argumentsp,c1,c2and returns the indices of the points with respect to CM-vertex regions in the standard basic triangle,rel.vert.triCMtakes argumentsp,triand returns the indices of the points with respect to CM-vertex regions in a triangle,rel.vert.std.tritakes argumentsp,Mand returns the indices of the points with respect to $M$-vertex regions in the standard equilateral triangle, andrel.vert.std.triCMtakes argumentpand returns the indices of the points with respect to CM-vertex regions in the standard equilateral triangle.
The function rel.verts.tri takes arguments Xp,tri,M for the data set, triangle,
and the center, respectively,
and returns the indices of the $M$-vertex regions in a triangle
where the points in a give data set reside in.
This function is the multiple point (or set) version of rel.vert.tri.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); n<-5 #try also n<-10, 50, or 100 set.seed(1) Xp<-runif.tri(n,Tr)$g M<-c(1.6,1.0) #try also M<-as.numeric(runif.tri(1,Tr)$g) (rv<-rel.verts.tri(Xp,Tr,M)) #> $rv #> [1] 1 2 3 1 1 #> #> $tri #> [,1] [,2] #> vertex 1 1.0 1 #> vertex 2 2.0 0 #> vertex 3 1.5 2
We can generate $n=$ r n points inside the triangle and
provide the scatterplot of these points labeled according to the vertex region
they reside in,
and also plot the vertex regions.
The resulting figure will be identical to the one in the examples of rel.vert.tri
Type ? rel.verts.tri for the code to generate the corresponding figure.
The following functions are special cases of rel.verts.tri, where
rel.verts.tritakes the same arguments and returns the same output as and is an alternate torel.verts.triwhen $M$ is not the circumcenter,rel.verts.tri.nondegPEtakes argumentsXp,tri,r,cent=1for the data set, triangle, the expansion parameter, and the non-degeneracy center (with default being the first center), respectively, and returns the indices of the data points with respect to M-vertex regions in a triangle where $M$ is one of the non-degeneracy centers of the triangle, andrel.verts.triCMtakes the argumentsXp,triand returns the indices of the data points with respect to CM-vertex regions in a triangle.
The $M$-vertex regions for any $M$ in the interior of the triangle is by default defined with the extensions of the
lines combining the vertices to the center $M$ (i.e., projections of M to the edges) as in the function prj.cent2edges.
Such vertex regions are only defined for a central point $M$ in the interior of the triangle.
However, using the circumcenter (CC), we define the vertex regions with the orthogonal projections from CC to the edges
(as this is a natural construction for the CC-vertex regions, because the corresponding partition of the triangle constitutes
vertex regions, where each vertex region contains points in the triangle closest to that vertex).
Note also that CC can be inside, on the boundary, or outside of the triangle
when the triangle is acute, right, or obtuse, respectively.
The function rel.vert.triCC(P,Tr) takes arguments p,tri and
returns the index of the CC-vertex region in a triangle tri where the point p resides.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); P<-c(1.3,1.2) rel.vert.triCC(P,Tr) #> $rv #> [1] 1 #> #> $tri #> [,1] [,2] #> vertex 1 1.0 1 #> vertex 2 2.0 0 #> vertex 3 1.5 2
We can illustrate the CC-vertex regions in an annotated plot,
type ? rel.vert.triCC for the code to generate this plot.
This plot will be as in the one in the examples of rel.verts.triCC without the scatter plot of the data points.
The function rel.vert.basic.triCC is a special case of the rel.vert.triCC function,
takes the arguments p,c1,c2 and return
the CC-vertex regions in the standard basic triangle.
The function rel.verts.triCC takes arguments Xp,tri
and returns the indices of the CC-vertex regions in a triangle tri where the points in a give data set Xp reside in.
This function is the multiple point (or set) version of rel.vert.triCC.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); n<-5 #try also n<-10, 50, or 100 set.seed(1) Xp<-runif.tri(n,Tr)$g (rv<-rel.verts.triCC(Xp,Tr)) #> $rv #> [1] 1 1 3 1 1 #> #> $tri #> [,1] [,2] #> vertex 1 1.0 1 #> vertex 2 2.0 0 #> vertex 3 1.5 2
We generate $n=$ r n points inside the triangle and provide the scatterplot of these points
labeled according to the CC-vertex region they reside in,
and also plot the CC-vertex regions using the below code.
Type also ? rel.verts.triCC for the code to generate this figure.
CC<-circumcenter.tri(Tr) D1<-(B+C)/2; D2<-(A+C)/2; D3<-(A+B)/2; Ds<-rbind(D1,D2,D3) Xlim<-range(Tr[,1],Xp[,1],CC[1]) Ylim<-range(Tr[,2],Xp[,2],CC[2]) xd<-Xlim[2]-Xlim[1] yd<-Ylim[2]-Ylim[1] plot(Tr,pch=".",asp=1,xlab="",ylab="", main="Scatterplot of data points with the CC-vertex regions", axes=TRUE,xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05)) polygon(Tr) points(Xp,pch=".",col=1) L<-matrix(rep(CC,3),ncol=2,byrow=TRUE); R<-Ds segments(L[,1], L[,2], R[,1], R[,2], lty=2) xc<-Tr[,1] yc<-Tr[,2] txt.str<-c("rv=1","rv=2","rv=3") text(xc,yc,txt.str) txt<-rbind(CC,Ds) xc<-txt[,1]+c(.04,.04,-.03,0) yc<-txt[,2]+c(-.07,.04,.06,-.08) txt.str<-c("CC","D1","D2","D3") text(xc,yc,txt.str) text(Xp,labels=factor(rv$rv))
Functions Related to the Edge Regions
The function rel.edge.tri takes arguments p,tri,M where
p, is a 2D point for whichM-edge region it resides in is to be determined in the triangletri,tri, is a $3 \times 2$ matrix with each row representing a vertex of the triangle.Mis a 2D point in Cartesian coordinates or a 3D point in barycentric coordinates which serves as a center in the interior of the triangletri.
This function returns the index of the M-edge region in a triangle tri that contains the point p.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); P<-c(1.4,1.2) M<-c(1.6,1.2) #try also set.seed(1234); M<-as.numeric(runif.tri(1,Tr)$g) rel.edge.tri(P,Tr,M) #> $re #> [1] 2 #> #> $tri #> [,1] [,2] #> A 1.0 1 #> B 2.0 0 #> C 1.5 2 #> #> $desc #> [1] "Edge labels are AB=3, BC=1, and AC=2"
We can illustrate the CC-vertex regions in an annotated plot,
type ? rel.edge.tri for the code to generate this plot.
This plot will be as in the examples of rel.edges.tri without the scatter plot of the data points.
The following functions are special cases of rel.edge.tri, where
rel.edge.basic.tritakes the argumentsp,c1,c2,Mand returns the same output for the standard basic triangle,rel.edge.basic.triCMtakes the argumentsp,c1,c2and returns the indices of the points with respect to CM-edge regions in the standard basic triangle,edge.reg.triCMtakes the argumentsp,triand returns the triangular edge region for a given data point, andrel.edge.triCMtakes the argumentsp,triand returns the indices of the points with respect to CM-edge regions in a triangletri.
The function rel.edges.tri takes arguments Xp,tri,M and
returns the indices of the $M$-edge regions in a triangle tri where the points in a give data set Xp reside in.
This function is the multiple point (i.e. set of points) version of rel.edge.tri.
A<-c(1,1); B<-c(2,0); C<-c(1.5,2); Tr<-rbind(A,B,C); n<-5 #try also n<-10, 50, or 100 set.seed(1) Xp<-runif.tri(n,Tr)$g M<-c(1.6,1.2) #try also M<-as.numeric(runif.tri(1,Tr)$g) (re<-rel.edges.tri(Xp,Tr,M)) #> $re #> [1] 3 3 2 3 2 #> #> $tri #> [,1] [,2] #> A 1.0 1 #> B 2.0 0 #> C 1.5 2 #> #> $desc #> [1] "Edge labels are AB=3, BC=1, and AC=2"
We generate $n=$ r n points inside the triangle and provide the scatterplot of these points
labeled according to the edge region they reside in,
and also plot the edge regions using the below code.
Type also ? rel.edges.tri for the code to generate this figure.
D1<-(B+C)/2; D2<-(A+C)/2; D3<-(A+B)/2; Ds<-rbind(D1,D2,D3) Xlim<-range(Tr[,1],Xp[,1]) Ylim<-range(Tr[,2],Xp[,2]) xd<-Xlim[2]-Xlim[1] yd<-Ylim[2]-Ylim[1] if (dimension(M)==3) {M<-bary2cart(M,Tr)} #need to run this when M is given in barycentric coordinates plot(Tr,pch=".",xlab="",ylab="", main="Scatterplot of data points with the M-edge regions",axes=TRUE, xlim=Xlim+xd*c(-.05,.05),ylim=Ylim+yd*c(-.05,.05)) polygon(Tr) points(Xp,pch=".",col=1) L<-Tr; R<-rbind(M,M,M) segments(L[,1], L[,2], R[,1], R[,2], lty=2) xc<-Tr[,1]+c(-.02,.03,.02) yc<-Tr[,2]+c(.02,.02,.04) txt.str<-c("A","B","C") text(xc,yc,txt.str) txt<-rbind(M,Ds) xc<-txt[,1]+c(.05,.06,-.05,-.02) yc<-txt[,2]+c(.03,.03,.05,-.08) txt.str<-c("M","re=1","re=2","re=3") text(xc,yc,txt.str) text(Xp,labels=factor(re$re))
The function rel.edges.triCM is a special case of rel.edges.tri, and uses the center of mass
to construct the edge regions.
References
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