# Incidence matrix for Central Similarity Proximity Catch Digraphs (CS-PCDs) - multiple triangle case Returns the incidence matrix of Central Similarity Proximity Catch Digraph (CS-PCD) whose vertices are the data points in `Xp` in the multiple triangle case. CS proximity regions are defined with respect to the Delaunay triangles based on `Yp` points with expansion parameter $t>0$ and edge regions in each triangle are based on the center $M=(\alpha,\beta,\gamma)$ in barycentric coordinates in the interior of each Delaunay triangle (default for $M=(1,1,1)$ which is the center of mass of the triangle). Each Delaunay triangle is first converted to an (nonscaled) basic triangle so that `M` will be the same type of center for each Delaunay triangle (this conversion is not necessary when `M` is $CM$). Convex hull of `Yp` is partitioned by the Delaunay triangles based on `Yp` points (i.e., multiple triangles are the set of these Delaunay triangles whose union constitutes the convex hull of `Yp` points). For the incidence matrix loops are allowed, so the diagonal entries are all equal to 1. See (if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="ceyhan:Phd-thesis,ceyhan:arc-density-CS,ceyhan:test2014;textual",package="pcds",cached_env=.Rdpack.currefs) ) for more on CS-PCDs. Also see (if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="okabe:2000,ceyhan:comp-geo-2010,sinclair:2016;textual",package="pcds",cached_env=.Rdpack.currefs) ) for more on Delaunay triangulation and the corresponding algorithm. ```r inci.matCS(Xp, Yp, t, M = c(1, 1, 1)) ``` ## Arguments - `Xp`: A set of 2D points which constitute the vertices of the CS-PCD. - `Yp`: A set of 2D points which constitute the vertices of the Delaunay triangles. - `t`: A positive real number which serves as the expansion parameter in CS proximity region. - `M`: A 3D point in barycentric coordinates which serves as a center in the interior of each Delaunay triangle, default for $M=(1,1,1)$ which is the center of mass of each triangle. ## Returns Incidence matrix for the CS-PCD with vertices being 2D data set, `Xp`. CS proximity regions are constructed with respect to the Delaunay triangles and `M`-edge regions. ## Examples ```r #nx is number of X points (target) and ny is number of Y points (nontarget) nx<-20; ny<-5; #try also nx<-40; ny<-10 or nx<-1000; ny<-10; set.seed(1) Xp<-cbind(runif(nx,0,1),runif(nx,0,1)) 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)) M<-c(1,1,1) #try also M<-c(1,2,3) t<-1.5 #try also t<-2 IM<-inci.matCS(Xp,Yp,t,M) IM dom.num.greedy(IM) #try also dom.num.exact(IM) #takes a very long time for large nx, try smaller nx Idom.num.up.bnd(IM,3) #takes a very long time for large nx, try smaller nx ``` ## References if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_all_ref(.Rdpack.currefs) ## See Also `inci.matCStri`, `inci.matCSstd.tri`, `inci.matAS`, and `inci.matPE` ## Author(s) Elvan Ceyhan

inci.matCS function