[R-sig-Geo] Calculating 95th percentile within polygons

[Edzer Pebesma]

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.pebesma at wwu.de