{"id":595,"date":"2014-04-21T15:16:36","date_gmt":"2014-04-21T20:16:36","guid":{"rendered":"http:\/\/homepages.uc.edu\/~yaozo\/wordpress\/?p=595"},"modified":"2014-04-21T15:16:36","modified_gmt":"2014-04-21T20:16:36","slug":"image-plot-for-irregular-spatial-data","status":"publish","type":"post","link":"https:\/\/zhuoyao.net\/index.php\/2014\/04\/21\/image-plot-for-irregular-spatial-data\/","title":{"rendered":"Image plot for irregular spatial data."},"content":{"rendered":"\n\n\n
quilt.plot<\/td>\nR Documentation<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Image plot for irregular spatial data.<\/h2>\n

Description<\/h3>\n

Given a vector of z values associated with 2-d locations this function produces an image-like plot where the locations are discretized to a grid and the z values are coded as a color level from a color scale.<\/p>\n

Usage<\/h3>\n
quilt.plot(x, y, z, nx = 64, ny = 64,nrow=nx, ncol=ny,  grid = NULL, \n                     add.legend=TRUE,add=FALSE,col=tim.colors(256),...)\n<\/pre>\n
Arguments<\/h3>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
x<\/code><\/td>\nA vector of the x coordinates of the locations -or- a a 2 column matrix of the x-y coordinates.<\/td>\n<\/tr>\n
y<\/code><\/td>\nA vector of the y coordinates -or- if the locations are passed in x the z vector<\/td>\n<\/tr>\n
z<\/code><\/td>\nValues of the variable to be plotted.<\/td>\n<\/tr>\n
nx<\/code><\/td>\nNumber of grid boxes in x if a grid is not specified.<\/td>\n<\/tr>\n
ny<\/code><\/td>\nNumber of grid boxes in y.<\/td>\n<\/tr>\n
nrow<\/code><\/td>\nNumber of grid boxes in x.<\/td>\n<\/tr>\n
ncol<\/code><\/td>\nNumber of grid boxes in y.<\/td>\n<\/tr>\n
grid<\/code><\/td>\nA grid in the form of a\u00a0grid list<\/code>.<\/td>\n<\/tr>\n
add.legend<\/code><\/td>\nIf TRUE a legend color strip is added<\/td>\n<\/tr>\n
add<\/code><\/td>\nIf FALSE add to existing plot.<\/td>\n<\/tr>\n
col<\/code><\/td>\nColor scale for the image, the default is tim.colors \u2013 a pleasing spectrum.<\/td>\n<\/tr>\n
...<\/code><\/td>\narguments to be passed to the image.plot function<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Details<\/h3>\n
This function combines the discretization to an image by the function\u00a0as.image<\/code>\u00a0and is then graphed by\u00a0image.plot<\/code>. Locations that fall into the same grid box will have their z values averaged.<\/p>\n
A similar function exists in the lattice package and produces good looking plots. The advantage of this fields version is that it uses the standard R graphics functions and is written in R code. Also, the aggregation to average values for z values in the same grid box allows for different choices of grids. If two locations are very close, separating them could result in very small boxes.<\/p>\n
As always, legend placement is never completely automatic. Place the legend independently for more control, perhaps usingimage.plot<\/code>\u00a0in tandem with\u00a0split.screen<\/code>\u00a0or enlarging the plot margin See\u00a0help(image.plot)<\/code>\u00a0for examples of this function and these strategies.<\/p>\n
Author(s)<\/h3>\n
D.Nychka<\/p>\n
See Also<\/h3>\n
as.image, image.plot, lattice, persp, drape.plot<\/p>\n
Examples<\/h3>\n
data( ozone2)\n# plot 16 day of ozone data set\n\nquilt.plot( ozone2$lon.lat, ozone2$y[16,])\nUS( add=TRUE, col=\"grey\", lwd=2)\n\n#\n# and ... if you are fussy \n# do it again \n# quilt.plot( ozone2$lon.lat, ozone2$y[16,],add=TRUE)\n# to draw over the state boundaries. \n#\n\n### adding a common legend strip \"by hand\"\n## and a custom color table\n\ncoltab<- two.colors( 256, middle=\"grey50\" )\n\npar( oma=c( 0,0,0,5)) # save some room for the legend\nset.panel(2,2)\nzr<- range( ozone2$y, na.rm=TRUE)\n\nfor( k in 1:4){\nquilt.plot( ozone2$lon.lat, ozone2$y[15+k,], add.legend=FALSE,\n zlim=zr, col=coltab, nrow=40, ncol=40)\nUS( add=TRUE)\n}\npar( oma=c(0,0,0,1))\nimage.plot(zlim=zr,legend.only=TRUE, col=coltab)\n# may have to adjust number of spaces in oma to make this work.\n\n<\/pre>\n
Results<\/h3>\n
R version 3.0.2 (2013-09-25) -- \"Frisbee Sailing\"\nCopyright (C) 2013 The R Foundation for Statistical Computing\nPlatform: x86_64-unknown-linux-gnu (64-bit)\n\nR is free software and comes with ABSOLUTELY NO WARRANTY.\nYou are welcome to redistribute it under certain conditions.\nType 'license()' or 'licence()' for distribution details.\n\nR is a collaborative project with many contributors.\nType 'contributors()' for more information and\n'citation()' on how to cite R or R packages in publications.\n\nType 'demo()' for some demos, 'help()' for on-line help, or\n'help.start()' for an HTML browser interface to help.\nType 'q()' to quit R.\n\n> library(fields)\nLoading required package: spam\nSpam version 0.29-3 (2013-04-23) is loaded.\nType 'help( Spam)' or 'demo( spam)' for a short introduction \nand overview of this package.\nHelp for individual functions is also obtained by adding the\nsuffix '.spam' to the function name, e.g. 'help( chol.spam)'.\n\nAttaching package: 'spam'\n\nThe following objects are masked from 'package:base':\n\n    backsolve, forwardsolve\n\nLoading required package: maps\n> png(filename=\"\/home\/oogasawa\/snapshot\/RGM3\/R_CC\/result\/fields\/quilt.plot.Rd_%03d_medium.png\", width=480, height=480)\n> ### Name: quilt.plot\n> ### Title: Image plot for irregular spatial data.\n> ### Aliases: quilt.plot\n> ### Keywords: hplot\n> \n> ### ** Examples\n> \n> \n> data( ozone2)\n> # plot 16 day of ozone data set\n> \n> quilt.plot( ozone2$lon.lat, ozone2$y[16,])\n> US( add=TRUE, col=\"grey\", lwd=2)\n> \n> #\n> # and ... if you are fussy \n> # do it again \n> # quilt.plot( ozone2$lon.lat, ozone2$y[16,],add=TRUE)\n> # to draw over the state boundaries. \n> #\n> \n> ### adding a common legend strip \"by hand\"\n> ## and a custom color table\n> \n> coltab<- two.colors( 256, middle=\"grey50\" )\n> \n> par( oma=c( 0,0,0,5)) # save some room for the legend\n> set.panel(2,2)\nplot window will lay out plots in a 2 by 2 matrix \n> zr<- range( ozone2$y, na.rm=TRUE)\n> \n> for( k in 1:4){\n+ quilt.plot( ozone2$lon.lat, ozone2$y[15+k,], add.legend=FALSE,\n+  zlim=zr, col=coltab, nrow=40, ncol=40)\n+ US( add=TRUE)\n+ }\n> par( oma=c(0,0,0,1))\n> image.plot(zlim=zr,legend.only=TRUE, col=coltab)\n> # may have to adjust number of spaces in oma to make this work.\n> \n>   \n> \n> \n> \n> \n> dev.off()\nnull device \n          1 \n>\n<\/pre>\n
 <\/p>\n\n\n\n
\"\"<\/a>\"\"<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"
quilt.plot R Documentation Image plot for irregular spatial data. Description Given a vector of z values associated with 2-d locations this function produces an image-like… <\/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-595","post","type-post","status-publish","format-standard","hentry","category-r"],"_links":{"self":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/posts\/595","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=595"}],"version-history":[{"count":0,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/posts\/595\/revisions"}],"wp:attachment":[{"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/media?parent=595"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/categories?post=595"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/zhuoyao.net\/index.php\/wp-json\/wp\/v2\/tags?post=595"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}