{"id":611,"date":"2014-04-21T15:59:58","date_gmt":"2014-04-21T20:59:58","guid":{"rendered":"http:\/\/homepages.uc.edu\/~yaozo\/wordpress\/?p=611"},"modified":"2014-04-21T15:59:58","modified_gmt":"2014-04-21T20:59:58","slug":"neighbourhood-graphs","status":"publish","type":"post","link":"https:\/\/zhuoyao.net\/index.php\/2014\/04\/21\/neighbourhood-graphs\/","title":{"rendered":"Neighbourhood Graphs"},"content":{"rendered":"\n\n\n
neig<\/td>\nR Documentation<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Neighbourhood Graphs<\/h2>\n

Description<\/h3>\n

neig<\/code>\u00a0creates objects of class\u00a0neig<\/code>\u00a0with :
\na list of edges
\na binary square matrix
\na list of vectors of neighbours
\nan integer (linear and circular graphs)
\na data frame of polygons (area)<\/p>\n

scores.neig returns the eigenvectors of neighbouring,
\northonormalized scores (null average, unit variance 1\/n and null covariances) of maximal autocorrelation.<\/p>\n

nb2neig returns an object of class\u00a0neig<\/code>\u00a0using an object of class\u00a0nb<\/code>\u00a0in the library ‘spdep’<\/p>\n

neig2nb returns an object of class\u00a0nb<\/code>\u00a0using an object of class\u00a0neig<\/code><\/p>\n

neig2mat returns the incidence matrix between edges (1 = neighbour ; 0 = no neighbour)<\/p>\n

neig.util.GtoL and neig.util.LtoG are utilities.<\/p>\n

Usage<\/h3>\n
neig(list = NULL, mat01 = NULL, edges = NULL,\n    n.line = NULL, n.circle = NULL, area = NULL)\n\nscores.neig  (obj) \n## S3 method for class 'neig'\nprint(x, ...) \n## S3 method for class 'neig'\nsummary(object, ...)\nnb2neig (nb)\nneig2nb (neig)\nneig2mat (neig)\n<\/pre>\n
Arguments<\/h3>\n\n\n\n\n\n\n\n\n\n\n\n
list<\/code><\/td>\na list which each component gives the number of neighbours<\/td>\n<\/tr>\n
mat01<\/code><\/td>\na symmetric square matrix of 0-1 values<\/td>\n<\/tr>\n
edges<\/code><\/td>\na matrix of 2 columns with integer values giving a list of edges<\/td>\n<\/tr>\n
n.line<\/code><\/td>\nthe number of points for a linear plot<\/td>\n<\/tr>\n
n.circle<\/code><\/td>\nthe number of points for a circular plot<\/td>\n<\/tr>\n
area<\/code><\/td>\na data frame containing a polygon set (see area.plot)<\/td>\n<\/tr>\n
nb<\/code><\/td>\nan object of class ‘nb’<\/td>\n<\/tr>\n
neig, x, obj, object<\/code><\/td>\nan object of class ‘neig’<\/td>\n<\/tr>\n
...<\/code><\/td>\nfurther arguments passed to or from other methods<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Author(s)<\/h3>\n
Daniel Chessel<\/p>\n
References<\/h3>\n
Thioulouse, J., D. Chessel, and S. Champely. 1995. Multivariate analysis of spatial patterns: a unified approach to local and global structures.\u00a0Environmental and Ecological Statistics<\/em>,\u00a02<\/b>, 1\u201314.<\/p>\n
Examples<\/h3>\n
data(mafragh)\nif (require(deldir, quietly=TRUE)) {\n    par(mfrow = c(2,1))\n    provi <- deldir(mafragh$xy)\n    provi.neig <- neig(edges = as.matrix(provi$delsgs[,5:6]))\n    \n    s.label(mafragh$xy, neig = provi.neig, inc = FALSE, \n        addax = FALSE, clab = 0, cnei = 2)\n    dist <- apply(provi.neig, 1, function(x) \n        sqrt(sum((mafragh$xy[x[1],] - mafragh$xy[x[2],])^2)))\n    #hist(dist, nclass = 50)\n    mafragh.neig <- neig(edges = provi.neig[dist<50,])\n    s.label(mafragh$xy, neig = mafragh.neig, inc = FALSE, \n        addax = FALSE, clab = 0, cnei = 2)\n    par(mfrow = c(1,1))\n    \n    data(irishdata)\n    irish.neig <- neig(area = irishdata$area)\n      summary(irish.neig)\n      print(irish.neig)\n    s.label(irishdata$xy, neig = irish.neig, cneig = 3,\n        area = irishdata$area, clab = 0.8, inc = FALSE)\n    \n    irish.scores <- scores.neig(irish.neig)\n    par(mfrow = c(2,3))\n    for (i in 1:6) s.value(irishdata$xy, irish.scores[,i],\n        inc = FALSE, grid = FALSE, addax = FALSE,\n        neig = irish.neig,\n        csi = 2, cleg = 0, sub = paste(\"Eigenvector \",i), csub = 2)\n    par(mfrow = c(1,1))\n    \n    a.neig <- neig(n.circle = 16)\n    a.scores <- scores.neig(a.neig)\n    xy <- cbind.data.frame(cos((1:16) * pi \/ 8), sin((1:16) * pi \/ 8))\n    par(mfrow = c(4,4))\n    for (i in 1:15) s.value(xy, a.scores[,i], neig = a.neig, \n        csi = 3, cleg = 0)\n    par(mfrow = c(1,1))\n    \n    a.neig <- neig(n.line = 28)\n    a.scores <- scores.neig(a.neig)\n    par(mfrow = c(7,4))\n    par(mar = c(1.1,2.1,0.1,0.1))\n    for (i in 1:27) barplot(a.scores[,i], col = grey(0.8))\n}\npar(mfrow = c(1,1))\n\nif (require(maptools, quiet = TRUE) & require(spdep, quiet = TRUE)) {\n    data(columbus)\n    par(mfrow = c(2,1))\n    par(mar = c(0.1,0.1,0.1,0.1))\n    plot(col.gal.nb, coords)\n    s.label(data.frame(coords), neig = neig(list = col.gal.nb),\n        inc = FALSE, clab = 0.6, cneig = 1)\n    par(mfrow = c(1,1))\n    \n    data(mafragh)\n    maf.rel <- relativeneigh(as.matrix(mafragh$xy))\n    maf.rel <- graph2nb(maf.rel)\n    s.label(mafragh$xy, neig = neig(list = maf.rel), inc = FALSE,\n        clab = 0, addax = FALSE, cne = 1, cpo = 2)\n    \n    par(mfrow = c(2,2))\n    w <- matrix(runif(100), 50, 2)\n    x.gab <- gabrielneigh(w)\n    x.gab <- graph2nb(x.gab)\n    s.label(data.frame(w), neig = neig(list = x.gab), inc = FALSE,\n        clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = \"relative\")\n    x.rel <- relativeneigh(w)\n    x.rel <- graph2nb(x.rel)\n    s.label(data.frame(w), neig = neig(list = x.rel), inc = FALSE,\n        clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = \"Gabriel\")\n    k1 <- knn2nb(knearneigh(w))\n    s.label(data.frame(w), neig = neig(list = k1), inc = FALSE,\n        clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = \"k nearest neighbours\")\n    \n    all.linked <- max(unlist(nbdists(k1, w)))\n    z <- dnearneigh(w, 0, all.linked)\n    s.label(data.frame(w), neig = neig(list = z), inc = FALSE,\n        clab = 0, addax = FALSE, cne = 1, cpo = 2, \n        sub = \"Neighbourhood contiguity by distance\")\n}\npar(mfrow = c(1,1))\n<\/pre>\n
Results<\/h3>\n
R version 3.0.2 (2013-09-25) -- \"Frisbee Sailing\"\nCopyright (C) 2013 The R Foundation for Statistical Computing\nPlatform: x86_64-unknown-linux-gnu (64-bit)\n\nR is free software and comes with ABSOLUTELY NO WARRANTY.\nYou are welcome to redistribute it under certain conditions.\nType 'license()' or 'licence()' for distribution details.\n\nR is a collaborative project with many contributors.\nType 'contributors()' for more information and\n'citation()' on how to cite R or R packages in publications.\n\nType 'demo()' for some demos, 'help()' for on-line help, or\n'help.start()' for an HTML browser interface to help.\nType 'q()' to quit R.\n\n> library(ade4)\n\nAttaching package: 'ade4'\n\nThe following object is masked from 'package:base':\n\n    within\n\n> png(filename=\"\/home\/oogasawa\/snapshot\/RGM3\/R_CC\/result\/ade4\/neig.Rd_%03d_medium.png\", width=480, height=480)\n> ### Name: neig\n> ### Title: Neighbourhood Graphs\n> ### Aliases: neig neig.util.GtoL neig.util.LtoG print.neig summary.neig\n> ###   scores.neig nb2neig neig2nb neig2mat\n> ### Keywords: utilities\n> \n> ### ** Examples\n> \n> data(mafragh)\n> if (require(deldir, quietly=TRUE)) {\n+     par(mfrow = c(2,1))\n+     provi <- deldir(mafragh$xy)\n+     provi.neig <- neig(edges = as.matrix(provi$delsgs[,5:6]))\n+     \n+     s.label(mafragh$xy, neig = provi.neig, inc = FALSE, \n+         addax = FALSE, clab = 0, cnei = 2)\n+     dist <- apply(provi.neig, 1, function(x) \n+         sqrt(sum((mafragh$xy[x[1],] - mafragh$xy[x[2],])^2)))\n+     #hist(dist, nclass = 50)\n+     mafragh.neig <- neig(edges = provi.neig[dist<50,])\n+     s.label(mafragh$xy, neig = mafragh.neig, inc = FALSE, \n+         addax = FALSE, clab = 0, cnei = 2)\n+     par(mfrow = c(1,1))\n+     \n+     data(irishdata)\n+     irish.neig <- neig(area = irishdata$area)\n+       summary(irish.neig)\n+       print(irish.neig)\n+     s.label(irishdata$xy, neig = irish.neig, cneig = 3,\n+         area = irishdata$area, clab = 0.8, inc = FALSE)\n+     \n+     irish.scores <- scores.neig(irish.neig)\n+     par(mfrow = c(2,3))\n+     for (i in 1:6) s.value(irishdata$xy, irish.scores[,i],\n+         inc = FALSE, grid = FALSE, addax = FALSE,\n+         neig = irish.neig,\n+         csi = 2, cleg = 0, sub = paste(\"Eigenvector \",i), csub = 2)\n+     par(mfrow = c(1,1))\n+     \n+     a.neig <- neig(n.circle = 16)\n+     a.scores <- scores.neig(a.neig)\n+     xy <- cbind.data.frame(cos((1:16) * pi \/ 8), sin((1:16) * pi \/ 8))\n+     par(mfrow = c(4,4))\n+     for (i in 1:15) s.value(xy, a.scores[,i], neig = a.neig, \n+         csi = 3, cleg = 0)\n+     par(mfrow = c(1,1))\n+     \n+     a.neig <- neig(n.line = 28)\n+     a.scores <- scores.neig(a.neig)\n+     par(mfrow = c(7,4))\n+     par(mar = c(1.1,2.1,0.1,0.1))\n+     for (i in 1:27) barplot(a.scores[,i], col = grey(0.8))\n+ }\ndeldir 0.0-22\n\n     PLEASE NOTE:  The components \"delsgs\" and \"summary\" of the \n     object returned by deldir() are now DATA FRAMES rather than \n     matrices (as they were prior to release 0.0-18). \n     See help(\"deldir\").\n \n     PLEASE NOTE: The process that deldir() uses for determining\n     duplicated points has changed from that used in version\n     0.0-9 of this package (and previously). See help(\"deldir\").\n\nNeigbourhood undirected graph\nVertices: 25 \nDegrees: 5 5 4 4 1 5 3 6 5 5 5 4 4 2 3 7 3 7 7 3 8 4 6 4 4 \nEdges (pairs of vertices): 57 \nS01 .\nS02 ..\nS03 ...\nS04 ....\nS05 .....\nS06 ..1...\nS07 ..11...\nS08 1.......\nS09 1........\nS10 1......11.\nS11 .1..1......\nS12 ..11..1.....\nS13 .1........1..\nS14 ..............\nS15 .....1.........\nS16 .1.....1.....1..\nS17 .1...........1.1.\nS18 .....1.1.1.....1..\nS19 .....1....1.1.1..1.\nS20 ..........1...1...1.\nS21 ..11.1..11.1.....1...\nS22 ...1....1...........1.\nS23 .1.....1....1..1.11....\nS24 1.......1............1..\nS25 1......1.......1.......1.\n> par(mfrow = c(1,1))\n> \n> if (require(maptools, quiet = TRUE) & require(spdep, quiet = TRUE)) {\n+     data(columbus)\n+     par(mfrow = c(2,1))\n+     par(mar = c(0.1,0.1,0.1,0.1))\n+     plot(col.gal.nb, coords)\n+     s.label(data.frame(coords), neig = neig(list = col.gal.nb),\n+         inc = FALSE, clab = 0.6, cneig = 1)\n+     par(mfrow = c(1,1))\n+     \n+     data(mafragh)\n+     maf.rel <- relativeneigh(as.matrix(mafragh$xy))\n+     maf.rel <- graph2nb(maf.rel)\n+     s.label(mafragh$xy, neig = neig(list = maf.rel), inc = FALSE,\n+         clab = 0, addax = FALSE, cne = 1, cpo = 2)\n+     \n+     par(mfrow = c(2,2))\n+     w <- matrix(runif(100), 50, 2)\n+     x.gab <- gabrielneigh(w)\n+     x.gab <- graph2nb(x.gab)\n+     s.label(data.frame(w), neig = neig(list = x.gab), inc = FALSE,\n+         clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = \"relative\")\n+     x.rel <- relativeneigh(w)\n+     x.rel <- graph2nb(x.rel)\n+     s.label(data.frame(w), neig = neig(list = x.rel), inc = FALSE,\n+         clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = \"Gabriel\")\n+     k1 <- knn2nb(knearneigh(w))\n+     s.label(data.frame(w), neig = neig(list = k1), inc = FALSE,\n+         clab = 0, addax = FALSE, cne = 1, cpo = 2, sub = \"k nearest neighbours\")\n+     \n+     all.linked <- max(unlist(nbdists(k1, w)))\n+     z <- dnearneigh(w, 0, all.linked)\n+     s.label(data.frame(w), neig = neig(list = z), inc = FALSE,\n+         clab = 0, addax = FALSE, cne = 1, cpo = 2, \n+         sub = \"Neighbourhood contiguity by distance\")\n+ }\nChecking rgeos availability: TRUE\n\nAttaching package: 'boot'\n\nThe following object is masked from 'package:lattice':\n\n    melanoma\n\n\nAttaching package: 'spdep'\n\nThe following object is masked from 'package:ade4':\n\n    mstree\n\n> par(mfrow = c(1,1))\n> \n> \n> \n> \n> \n> dev.off()\nnull device \n          1 \n>\n<\/pre>\n
 <\/p>\n\n\n\n
\"\"<\/a>\"\"<\/a>\"\"<\/a>\"\"<\/a>\"\"<\/a>\"\"<\/a>\"\"<\/a>\"\"<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"
neig R Documentation Neighbourhood Graphs Description neig\u00a0creates objects of class\u00a0neig\u00a0with : a list of edges a binary square matrix a list of vectors of neighbours… <\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-611","post","type-post","status-publish","format-standard","hentry","category-r"],"_links":{"self":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/posts\/611","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/comments?post=611"}],"version-history":[{"count":0,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/posts\/611\/revisions"}],"wp:attachment":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/media?parent=611"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/categories?post=611"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/tags?post=611"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}