Zia,You may want to rethink the question. Each realization has a 95 percentile within a particular polygon. Over the set of realizations of some aggregated value for a polygon, you can take a 95 percentile.
These are two different things. The first is a spatial aggregation, the second an aggregation over the (sampled) probability distribution.
On 10/18/2011 11:07 PM, Zia Ahmed wrote:
I am trying to calculating 95th percentile within polygons from a of set realizations - something like zonal statistics. How do I calculate 95 th percentile for each polygon over all realizations. Thanks Zia For example: data(meuse) data(meuse.grid) coordinates(meuse) <- ~x+y coordinates(meuse.grid) <- ~x+y # Simulation nsim=10 x <- krige(log(zinc)~1, meuse, meuse.grid, model = vgm(.59, "Sph", 874, .04), nmax=10, nsim=nsim) over(sr, x[,1:4], fn = mean) > over(sr, x[,1:4], fn = mean) sim1 sim2 sim3 sim4 r1 5.858169 5.792870 5.855246 5.868499 r2 5.588570 5.452744 5.596648 5.516289 r3 5.798087 5.860750 5.784194 5.848194 r4 NA NA NA NA # 95 th percentile at prediction grid: x<-as.data.frame(x) y95<-apply(x[3:nsim],1,stats::quantile,probs = 0.95,na.rm=TRUE) # 95 th percentile at each prediction grid On 10/18/2011 3:30 PM, Edzer Pebesma wrote:require(sp) r1 = cbind(c(180114, 180553, 181127, 181477, 181294, 181007, 180409, 180162, 180114), c(332349, 332057, 332342, 333250, 333558, 333676, 332618, 332413, 332349)) r2 = cbind(c(180042, 180545, 180553, 180314, 179955, 179142, 179437, 179524, 179979, 180042), c(332373, 332026, 331426, 330889, 330683, 331133, 331623, 332152, 332357, 332373)) r3 = cbind(c(179110, 179907, 180433, 180712, 180752, 180329, 179875, 179668, 179572, 179269, 178879, 178600, 178544, 179046, 179110), c(331086, 330620, 330494, 330265, 330075, 330233, 330336, 330004, 329783, 329665, 329720, 329933, 330478, 331062, 331086)) r4 = cbind(c(180304, 180403,179632,179420,180304), c(332791, 333204, 333635, 333058, 332791)) sr1=Polygons(list(Polygon(r1)),"r1") sr2=Polygons(list(Polygon(r2)),"r2") sr3=Polygons(list(Polygon(r3)),"r3") sr4=Polygons(list(Polygon(r4)),"r4") sr=SpatialPolygons(list(sr1,sr2,sr3,sr4)) srdf=SpatialPolygonsDataFrame(sr, data.frame(cbind(1:4,5:2), row.names=c("r1","r2","r3","r4"))) data(meuse) coordinates(meuse) = ~x+y plot(meuse) polygon(r1) polygon(r2) polygon(r3) polygon(r4) # retrieve mean heavy metal concentrations per polygon: # attribute means over each polygon, NA for empty over(sr, meuse[,1:4], fn = mean) # return the number of points in each polygon: sapply(over(sr, geometry(meuse), returnList = TRUE), length)
-- Edzer Pebesma Institute for Geoinformatics (ifgi), University of Münster Weseler Straße 253, 48151 Münster, Germany. Phone: +49 251 8333081, Fax: +49 251 8339763 http://ifgi.uni-muenster.de http://www.52north.org/geostatistics e.pebe...@wwu.de _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
