Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>",fig.width=6, fig.height=4, fig.align = "center")
## ----setup, message=FALSE, results='hide'-------------------------------------
library(pcds)
## -----------------------------------------------------------------------------
nx<-10; ny<-5; #try also nx<-40; ny<-10 or nx<-1000; ny<-20;
set.seed(123)
Xp<-cbind(runif(nx),runif(nx))
Yp<-cbind(runif(ny,0,.25),runif(ny,0,.25))+cbind(c(0,0,0.5,1,1),c(0,1,.5,0,1))
#try also Yp<-cbind(runif(ny,0,1),runif(ny,0,1))
## ----ADfig, eval=F, fig.cap="The scatterplot of the 2D artificial data set with two classes; black circles are class $X$ points and red triangles are class $Y$ points."----
# XYpts = rbind(Xp,Yp) #combined Xp and Yp
# lab=c(rep(1,nx),rep(2,ny))
# lab.fac=as.factor(lab)
# plot(XYpts,col=lab,pch=lab,xlab="x",ylab="y",main="Scatterplot of 2D Points from Two Classes")
## ----AD-DTfig, fig.cap="The scatterplot of the X points in the artificial data set together with the Delaunay triangulation of $Y$ points (dashed lines)."----
Xlim<-range(Xp[,1],Yp[,1])
Ylim<-range(Xp[,2],Yp[,2])
xd<-Xlim[2]-Xlim[1]
yd<-Ylim[2]-Ylim[1]
plot(Xp,xlab="x", ylab="y",xlim=Xlim+xd*c(-.05,.05),
ylim=Ylim+yd*c(-.05,.05),pch=".",cex=3,main="X points and Delaunay Triangulation of Y Points")
#now, we add the Delaunay triangulation based on $Y$ points
DT<-interp::tri.mesh(Yp[,1],Yp[,2],duplicate="remove")
interp::plot.triSht(DT, add=TRUE, do.points = TRUE)
## ----eval=F-------------------------------------------------------------------
# num.delaunay.tri(Yp)
# #> [1] 4
## -----------------------------------------------------------------------------
M<-"CC" #try also M<-c(1,1,1) #or M<-c(1,2,3)
## ----numarcsASpr1, eval=F, fig.cap="The number of arcs of AS-PCD at the Delaunay triangles based on the $Y$ points (dashed lines)."----
# Narcs = num.arcsAS(Xp,Yp,M)
# Narcs
# #> Call:
# #> num.arcsAS(Xp = Xp, Yp = Yp, M = M)
# #>
# #> Description:
# #> Number of Arcs of the AS-PCD with vertices Xp and Related Quantities for the Induced Subdigraphs for the Points in the Delaunay Triangles
#
# summary(Narcs)
# #> Call:
# #> num.arcsAS(Xp = Xp, Yp = Yp, M = M)
# #>
# #> Description of the output:
# #> Number of Arcs of the AS-PCD with vertices Xp and Related Quantities for the Induced Subdigraphs for the Points in the Delaunay Triangles
# #>
# #> Number of data (Xp) points in the convex hull of Yp (nontarget) points = 7
# #> Number of data points in the Delaunay triangles based on Yp points = 2 1 1 3
# #> Number of arcs in the entire digraph = 3
# #> Numbers of arcs in the induced subdigraphs in the Delaunay triangles = 0 0 0 3
# #> Areas of the Delaunay triangles (used as weights in the arc density of multi-triangle case):
# #> 0.2214646 0.2173192 0.2593852 0.2648197
# #>
# #> Indices of the vertices of the Delaunay triangles (each column refers to a triangle):
# #> [,1] [,2] [,3] [,4]
# #> [1,] 1 5 3 3
# #> [2,] 3 2 4 1
# #> [3,] 2 3 5 4
# #>
# #> Indices of the Delaunay triangles data points resides:
# #> 1 4 1 3 NA NA 4 NA 4 2
# plot(Narcs)
## ----eval=F-------------------------------------------------------------------
# IM<-inci.matAS(Xp,Yp,M)
# IM[1:6,1:6]
# #> [,1] [,2] [,3] [,4] [,5] [,6]
# #> [1,] 1 0 0 0 0 0
# #> [2,] 0 1 0 0 0 0
# #> [3,] 0 0 1 0 0 0
# #> [4,] 0 0 0 1 0 0
# #> [5,] 0 0 0 0 1 0
# #> [6,] 0 0 0 0 0 1
## ----eval=F-------------------------------------------------------------------
# dom.num.greedy(IM) #try also dom.num.exact(IM) #this might take a longer time for large nx (i.e. nx >= 19)
# #> $approx.dom.num
# #> [1] 8
# #>
# #> $ind.approx.mds
# #> [1] 9 1 10 5 8 4 3 6
## ----adASarcs1, fig.cap="The arcs of the AS-PCD for the 2D artificial data set using the CC-vertex regions together with the Delaunay triangles based on the $Y$ points (dashed lines)."----
plotASarcs(Xp,Yp,M,asp=1,xlab="",ylab="")
## ----adASpr1, fig.cap="The AS proximity regions for all $X$ points in the 2D artificial data set using the CC-vertex regions together with the Delaunay triangles based on the $Y$ points (dashed lines)."----
plotASregs(Xp,Yp,M,xlab="",ylab="")
## ----adASarcs2, eval=F, fig.cap="The arcs of the AS-PCD for the 2D artificial data set using the CC-vertex regions together with the Delaunay triangles based on the $Y$ points (dashed lines)."----
# Arcs<-arcsAS(Xp,Yp,M)
# Arcs
# #> Call:
# #> arcsAS(Xp = Xp, Yp = Yp, M = M)
# #>
# #> Type:
# #> [1] "Arc Slice Proximity Catch Digraph (AS-PCD) for 2D Points in Multiple Triangles with CC-Vertex Regions"
# summary(Arcs)
# #> Call:
# #> arcsAS(Xp = Xp, Yp = Yp, M = M)
# #>
# #> Type of the digraph:
# #> [1] "Arc Slice Proximity Catch Digraph (AS-PCD) for 2D Points in Multiple Triangles with CC-Vertex Regions"
# #>
# #> Vertices of the digraph = Xp
# #> Partition points of the region = Yp
# #>
# #> Selected tail (or source) points of the arcs in the digraph
# #> (first 6 or fewer are printed)
# #> [,1] [,2]
# #> [1,] 0.5281055 0.2460877
# #> [2,] 0.5514350 0.3279207
# #> [3,] 0.5514350 0.3279207
# #>
# #> Selected head (or end) points of the arcs in the digraph
# #> (first 6 or fewer are printed)
# #> [,1] [,2]
# #> [1,] 0.5514350 0.3279207
# #> [2,] 0.7883051 0.4533342
# #> [3,] 0.5281055 0.2460877
# #>
# #> Parameters of the digraph
# #> $center
# #> [1] "CC"
# #>
# #> Various quantities of the digraph
# #> number of vertices number of partition points
# #> 7.00000000 5.00000000
# #> number of triangles number of arcs
# #> 4.00000000 3.00000000
# #> arc density
# #> 0.07142857
# plot(Arcs, asp=1)
## -----------------------------------------------------------------------------
M<-c(1,1,1) #try also M<-c(1,2,3) #or M<-"CC"
r<-1.5 #try also r<-2 or r=1.25
## ----eval=F-------------------------------------------------------------------
# Narcs = num.arcsPE(Xp,Yp,r,M)
# summary(Narcs)
# #> Call:
# #> num.arcsPE(Xp = Xp, Yp = Yp, r = r, M = M)
# #>
# #> Description of the output:
# #> Number of Arcs of the PE-PCD with vertices Xp and Related Quantities for the Induced Subdigraphs for the Points in the Delaunay Triangles
# #>
# #> Number of data (Xp) points in the convex hull of Yp (nontarget) points = 7
# #> Number of data points in the Delaunay triangles based on Yp points = 2 1 1 3
# #> Number of arcs in the entire digraph = 3
# #> Numbers of arcs in the induced subdigraphs in the Delaunay triangles = 1 0 0 2
# #> Areas of the Delaunay triangles (used as weights in the arc density of multi-triangle case):
# #> 0.2214646 0.2173192 0.2593852 0.2648197
# #>
# #> Indices of the vertices of the Delaunay triangles (each column refers to a triangle):
# #> [,1] [,2] [,3] [,4]
# #> [1,] 1 5 3 3
# #> [2,] 3 2 4 1
# #> [3,] 2 3 5 4
# #>
# #> Indices of the Delaunay triangles data points resides:
# #> 1 4 1 3 NA NA 4 NA 4 2
#
# plot(Narcs)
## ----include=FALSE------------------------------------------------------------
IM<-inci.matPE(Xp,Yp,r,M)
head(IM)
## ----adPEarcs1, fig.cap="The arcs of the PE-PCD for the 2D artificial data set using the CM-vertex regions and expansion parameter $r=1.5$ together with the Delaunay triangles based on the $Y$ points (dashed lines)."----
plotPEarcs(Xp,Yp,r,M,xlab="",ylab="")
## ----adPEpr1, fig.cap="The PE proximity regions for all the points the 2D artificial data set using the CM-vertex regions and expansion parameter $r=1.5$ together with the Delaunay triangles based on the $Y$ points (dashed lines)."----
plotPEregs(Xp,Yp,r,M,xlab="",ylab="")
## ----adPEarcs2, eval=F, fig.cap="The arcs of the PE-PCD for the 2D artificial data set using the CM-vertex regions and expansion parameter $r=1.5$ together with the Delaunay triangles based on the $Y$ points (dashed lines)."----
# Arcs<-arcsPE(Xp,Yp,r,M)
# Arcs
# #> Call:
# #> arcsPE(Xp = Xp, Yp = Yp, r = r, M = M)
# #>
# #> Type:
# #> [1] "Proportional Edge Proximity Catch Digraph (PE-PCD) for 2D points in Multiple Triangles with Expansion parameter r = 1.5 and Center M = (1,1,1)"
# summary(Arcs)
# #> Call:
# #> arcsPE(Xp = Xp, Yp = Yp, r = r, M = M)
# #>
# #> Type of the digraph:
# #> [1] "Proportional Edge Proximity Catch Digraph (PE-PCD) for 2D points in Multiple Triangles with Expansion parameter r = 1.5 and Center M = (1,1,1)"
# #>
# #> Vertices of the digraph = Xp
# #> Partition points of the region = Yp
# #>
# #> Selected tail (or source) points of the arcs in the digraph
# #> (first 6 or fewer are printed)
# #> [,1] [,2]
# #> [1,] 0.4089769 0.6775706
# #> [2,] 0.5281055 0.2460877
# #> [3,] 0.5514350 0.3279207
# #>
# #> Selected head (or end) points of the arcs in the digraph
# #> (first 6 or fewer are printed)
# #> [,1] [,2]
# #> [1,] 0.2875775 0.9568333
# #> [2,] 0.5514350 0.3279207
# #> [3,] 0.5281055 0.2460877
# #>
# #> Parameters of the digraph
# #> $center
# #> [1] 1 1 1
# #>
# #> $`expansion parameter`
# #> [1] 1.5
# #>
# #> Various quantities of the digraph
# #> number of vertices number of partition points
# #> 7.00000000 5.00000000
# #> number of triangles number of arcs
# #> 4.00000000 3.00000000
# #> arc density
# #> 0.07142857
# plot(Arcs)
## ----eval=F-------------------------------------------------------------------
# PEarc.dens.test(Xp,Yp,r) #try also PEarc.dens.test(Xp,Yp,r,alt="l") or with alt="g"
# #>
# #> Large Sample z-Test Based on Arc Density of PE-PCD for Testing
# #> Uniformity of 2D Data ---
# #> without Convex Hull Correction
# #>
# #> data: Xp
# #> standardized arc density (i.e., Z) = -0.21983, p-value = 0.826
# #> alternative hypothesis: true (expected) arc density is not equal to 0.09712203
# #> 95 percent confidence interval:
# #> 0.04234726 0.14084889
# #> sample estimates:
# #> arc density
# #> 0.09159807
## ----eval=F-------------------------------------------------------------------
# PEdom.num(Xp,Yp,r,M) #try also PEdom.num(Xp,Yp,r=2,M)
# #> $dom.num
# #> [1] 5
# #>
# #> $ind.mds
# #> [1] 3 10 4 9 2
# #>
# #> $tri.dom.nums
# #> [1] 1 1 1 2
# PEdom.num.nondeg(Xp,Yp,r) #try also PEdom.num.nondeg(Xp,Yp,r=1.25)
# #> $dom.num
# #> [1] 5
# #>
# #> $ind.mds
# #> [1] 3 10 4 2 9
# #>
# #> $tri.dom.nums
# #> [1] 1 1 1 2
## ----eval=F-------------------------------------------------------------------
# PEdom.num.binom.test(Xp,Yp,r) #try also PEdom.num.binom.test(Xp,Yp,r,alt="g") or with alt="l"
# #>
# #> Large Sample Binomial Test based on the Domination Number of PE-PCD for
# #> Testing Uniformity of 2D Data ---
# #> without Convex Hull Correction
# #>
# #> data: Xp
# #> # of times domination number is <= 2 = 4, p-value = 0.5785
# #> alternative hypothesis: true Pr(Domination Number <=2) is not equal to 0.7413
# #> 95 percent confidence interval:
# #> 0.3976354 1.0000000
# #> sample estimates:
# #> domination number || Pr(domination number <= 2)
# #> 5 1
## ----eval=F-------------------------------------------------------------------
# PEdom.num.norm.test(Xp,Yp,r) #try also PEdom.num.norm.test(Xp,Yp,r,alt="g") or with alt="l"
# #>
# #> Normal Approximation to the Domination Number of PE-PCD for Testing
# #> Uniformity of 2D Data ---
# #> without Convex Hull Correction
# #>
# #> data: Xp
# #> standardized domination number (i.e., Z) = 1.1815, p-value = 0.2374
# #> alternative hypothesis: true expected domination number is not equal to 2.9652
# #> 95 percent confidence interval:
# #> 3.283383 6.716617
# #> sample estimates:
# #> domination number || Pr(domination number <= 2)
# #> 5 1
## -----------------------------------------------------------------------------
M<-c(1,1,1) #try also M<-c(1,2,3)
tau<-1.5 #try also tau<-2
## ----eval=F-------------------------------------------------------------------
# Narcs = num.arcsCS(Xp,Yp,tau,M)
# summary(Narcs)
# #> Call:
# #> num.arcsCS(Xp = Xp, Yp = Yp, t = tau, M = M)
# #>
# #> Description of the output:
# #> Number of Arcs of the CS-PCD with vertices Xp and Related Quantities for the Induced Subdigraphs for the Points in the Delaunay Triangles
# #>
# #> Number of data (Xp) points in the convex hull of Yp (nontarget) points = 7
# #> Number of data points in the Delaunay triangles based on Yp points = 2 1 1 3
# #> Number of arcs in the entire digraph = 3
# #> Numbers of arcs in the induced subdigraphs in the Delaunay triangles = 1 0 0 2
# #> Areas of the Delaunay triangles (used as weights in the arc density of multi-triangle case):
# #> 0.2214646 0.2173192 0.2593852 0.2648197
# #>
# #> Indices of the vertices of the Delaunay triangles (each column refers to a triangle):
# #> [,1] [,2] [,3] [,4]
# #> [1,] 1 5 3 3
# #> [2,] 3 2 4 1
# #> [3,] 2 3 5 4
# #>
# #> Indices of the Delaunay triangles data points resides:
# #> 1 4 1 3 NA NA 4 NA 4 2
# #>
# #plot(Narcs)
## ----include=FALSE------------------------------------------------------------
IM<-inci.matCS(Xp,Yp,tau,M)
head(IM)
## ----adCSarcs1, fig.cap="The arcs of the CS-PCD for the 2D artificial data set using the CM-edge regions and expansion parameter $t=1.5$ together with the Delaunay triangles based on the $Y$ points (dashed lines)."----
plotCSarcs(Xp,Yp,tau,M,xlab="",ylab="")
## ----adCSpr1, fig.cap="The CS proximity regions for all the points the 2D artificial data set using the CM-edge regions and expansion parameter $t=1.5$ together with the Delaunay triangles based on the $Y$ points (dashed lines)."----
plotCSregs(Xp,Yp,tau,M,xlab="",ylab="")
## ----adCSarcs2, eval=F, fig.cap="The arcs of the CS-PCD for the 2D artificial data set using the CM-edge regions and expansion parameter $t=1.5$ together with the Delaunay triangles based on the $Y$ points (dashed lines)."----
# Arcs<-arcsCS(Xp,Yp,tau,M)
# Arcs
# #> Call:
# #> arcsCS(Xp = Xp, Yp = Yp, t = tau, M = M)
# #>
# #> Type:
# #> [1] "Central Similarity Proximity Catch Digraph (CS-PCD) for 2D Points in the Multiple Triangles with Expansion Parameter t = 1.5 and Center M = (1,1,1)"
# summary(Arcs)
# #> Call:
# #> arcsCS(Xp = Xp, Yp = Yp, t = tau, M = M)
# #>
# #> Type of the digraph:
# #> [1] "Central Similarity Proximity Catch Digraph (CS-PCD) for 2D Points in the Multiple Triangles with Expansion Parameter t = 1.5 and Center M = (1,1,1)"
# #>
# #> Vertices of the digraph = Xp
# #> Partition points of the region = Yp
# #>
# #> Selected tail (or source) points of the arcs in the digraph
# #> (first 6 or fewer are printed)
# #> [,1] [,2]
# #> [1,] 0.4089769 0.6775706
# #> [2,] 0.5281055 0.2460877
# #> [3,] 0.5514350 0.3279207
# #>
# #> Selected head (or end) points of the arcs in the digraph
# #> (first 6 or fewer are printed)
# #> [,1] [,2]
# #> [1,] 0.2875775 0.9568333
# #> [2,] 0.5514350 0.3279207
# #> [3,] 0.5281055 0.2460877
# #>
# #> Parameters of the digraph
# #> $center
# #> [1] 1 1 1
# #>
# #> $`expansion parameter`
# #> [1] 1.5
# #>
# #> Various quantities of the digraph
# #> number of vertices number of partition points
# #> 7.00000000 5.00000000
# #> number of triangles number of arcs
# #> 4.00000000 3.00000000
# #> arc density
# #> 0.07142857
# plot(Arcs)
## ----eval=F-------------------------------------------------------------------
# CSarc.dens.test(Xp,Yp,tau) #try also CSarc.dens.test(Xp,Yp,tau,alt="l") or with alt="g"
# #>
# #> Large Sample z-Test Based on Arc Density of CS-PCD for Testing
# #> Uniformity of 2D Data ---
# #> without Convex Hull Correction
# #>
# #> data: Xp
# #> standardized arc density (i.e., Z) = 0.6039, p-value = 0.5459
# #> alternative hypothesis: true (expected) arc density is not equal to 0.06749794
# #> 95 percent confidence interval:
# #> 0.0252619 0.1473522
# #> sample estimates:
# #> arc density
# #> 0.08630702
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