swh:1:snp:33a53053e50f7abe7d281cc0c803be827debf4a3
Tip revision: 7cf45c0f508ea53b364f87f9cf8012247d9ecb2f authored by Edzer J. Pebesma on 21 October 2004, 12:52:44 UTC
version 0.9-17
version 0.9-17
Tip revision: 7cf45c0
sic2004.R
# compared to the original submission, two errors
# were found; one was corrected (dayx~x -> dayx~1), the other not.
# read csv files:
#sic.pred = read.csv("SIC2004_out.csv", header=F)
#names(sic.pred) = c("record", "x", "y")
#input = read.csv("SIC2004_input.csv", header=F)
#names(input) = c("record", "x", "y", "dayx")
#joker = read.csv("SIC2004_joker.csv", header=F)
#names(joker) = c("record", "x", "y", "dayx")
library(gstat)
data(sic2004) # load directly from R data base
input = sic.test[,1:4]
names(input) = c("record", "x", "y", "dayx")
joker = sic.test[,c(1,2,3,5)]
names(joker) = c("record", "x", "y", "dayx")
do.sic = function(sic.input, sic.output) {
require(gstat)
# calculate omnidirectional sample variogram, cutoff 500000, intervals 20000
sic.input = sic.input[sic.input$record != 838, ]
v.sample = variogram(dayx~1, ~x+y, input, width=2e4, cutoff=5e5)
# ^^^^^ the error: this should be sic.input
# ^ this error has been corrected
# initial spherical model for fit: nug=100, p.sill=400,range=4.5e5
initial.model = vgm(400, "Sph", 450000, 100)
# fit the nugget, sill and range to the sample variogram:
v.fitted = fit.variogram(v.sample, initial.model, fit.method = 2)
# function returns the output from the call to krige():
krige(dayx~1, ~x+y, sic.input, sic.output, model = v.fitted, nmax = 125)
}
# do the spatial interpolations:
output = do.sic(input, sic.pred)
joker.output = do.sic(joker, sic.pred)
# write csv files:
#write.table(output, "sic2004_output.csv",
# sep=",", col.names=FALSE, row.names=FALSE)
#write.table(joker.output, "sic2004_joker_output.csv",
# sep=",", col.names=FALSE, row.names=FALSE)
# calculate output:
# read csv files:
sic.stats = function(obs, pred) {
print("observed:")
x = obs
print(c(min=min(x), max=max(x), mean=mean(x), median=median(x), stddev=sqrt(var(x))))
print("predicted:")
x = pred
print(c(min=min(x), max=max(x), mean=mean(x), median=median(x), stddev=sqrt(var(x))))
print("error:")
x = pred - obs
print(c(mae=mean(abs(x)), me=mean(x), corr=cor(pred,obs), rmse=sqrt(mean(x^2))))
}
options(digits=4)
print("input data first")
sic.stats(sic.valid[,4], output$var1.pred)
print("joker with error variogram")
sic.stats(sic.valid[,4], joker.output$var1.pred)
##################
do.sic = function(sic.input, sic.output) {
require(gstat)
v.sample = variogram(dayx~1, ~x+y, input, width=2e4, cutoff=5e5)
initial.model = vgm(400, "Sph", 450000, 100)
v.fitted = fit.variogram(v.sample, initial.model, fit.method = 2)
krige(dayx~1, ~x+y, sic.input, sic.output, model = v.fitted, nmax = 125)
}
# do the spatial interpolations on a grid:
grid1 = do.sic(input, sic.grid)
grid2 = do.sic(joker, sic.grid)
grid1$v2 = grid2$var1.pred
grid1$v2.var = grid2$var1.var
grid1$var1.pred[grid1$var1.pred>200] = 205 # mask larger values!
grid1$v2[grid1$v2>200] = 205 # mask larger values!
greys = c(255, 247, 240, 228, 217, 203, 189, 169, 150, 132, 115, 99, 82, 60, 37, 0)
panel.sic = function(...){
panel.levelplot(...)
lpoints(sic.train$x, sic.train$y, pch=22, cex=.5, col=1)
lpoints(sic.pred$x, sic.pred$y, pch="+", cex=1, col=1)
}
cp1 = contourplot(z~x+y|name, map.to.lev(grid1,z=c(3,5),
ns=c("data set 1","data set 2")), asp=mapasp(grid1),
col.regions=rgb(greys/255,greys/255,greys/255),
at=50+(0:16)*10,region=TRUE,labels=FALSE,
xlab = "", ylab = "",scales=list(draw=F),panel=panel.sic)
cp2 = contourplot(sqrt(z)~x+y|name, map.to.lev(grid1,z=4,
ns="standard error"), asp=mapasp(grid1),scales=list(draw=F),
col.regions=rgb(greys/255,greys/255,greys/255),
region=TRUE,labels=FALSE, xlab = "", ylab = "", panel=panel.sic)
print(cp1, c(0,0,0.625,1), more=T)
print(cp2, c(0.625,0,1,1), more=F)
wireframe(var1.pred~x+y,grid2,asp=c(mapasp(grid2),0.5),
xlab="x",ylab="y",zlab="z",drape=T,
col.regions=gray(sqrt(seq(from=1.0, to=0.0, length=100))))