Hi ALL,

Can you please help me on how to determine the prediction error for ordinary 
kriging?Below are all the commands i used to generate the OK plot:

rsa2 <- readShapeSpatial("residentialsa", CRS("+proj=tmerc 
+lat_0=39.66666666666666 +lon_0=-8.131906111111112 +k=1 +x_0=0 +y_0=0 
+ellps=intl +units=m +no_defs"))
x2 <- readShapeSpatial("ptna2", CRS("+proj=tmerc +lat_0=39.66666666666666 
+lon_0=-8.131906111111112 +k=1 +x_0=0 +y_0=0 +ellps=intl +units=m +no_defs"))
bb <- bbox(rsa2)
cs <- c(1, 1)
cc <- bb[, 1] + (cs/2)
cd <- ceiling(diff(t(bb))/cs)
rsa2_grd <- GridTopology(cellcentre.offset = cc,cellsize = cs, cells.dim = cd)
getClass("SpatialGrid")
p4s <- CRS(proj4string(rsa2))
x2_SG <- SpatialGrid(rsa2_grd, proj4string = p4s)
x2_SP <- SpatialPoints(cbind(x2$X, x2$Y))
v <- variogram(log1p(tsport_ace) ~ 1, x2, cutoff=100, width=9)
te<- fit.variogram(v,vgm(0.0437, "Exp", 26, 0))
y <- krige(tsport_ace~1, x2, x2_SG, model = ve.fit)
spplot(y, 1, col.regions = bpy.colors(100), sp.layout = list("sp.lines",as(rsa2, 
"SpatialLines"),no.clip = TRUE))

 I'm looking forward to your response. Thanks.

Best regards,
Pearl dela Cruz


      
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