Hi
Normally I use the R+SAGA to calculate the IDW and create a raster, with
this follow code. I change the radius input with a loop formula to create
several raster and after check the best result (I am studying a oak forest
with low density)
radii.list <- as.list(c(5, 10, 15, 20, 25, 30))
for(i in 1:length(radii.list)){
rsaga.geoprocessor(lib="grid_gridding", module=0,
param=list(GRID=set.file.extension(paste("DEM_iw",radii.list[[i]],sep=""),".
sgrd"),
SHAPES="ground.shp", FIELD=0, TARGET= 0, POWER=1.0,
RADIUS=radii.list[[i]], NPOINTS=20, USER_CELL_SIZE=dem.pixelsize,
[EMAIL PROTECTED],1], [EMAIL PROTECTED],2],
[EMAIL PROTECTED],1], [EMAIL PROTECTED],2]))
}
After I have 6 raster (DEM_idw_5.sgrd, DEM_idw_10.sgrd, DEM_idw_15.sgrd,
DEM_idw_20.sgrd, etc. etc.). I check hand-made the best IDW. I don't know is
It possible to improve this formula with RMSE in gstat and calculate the
best power.
Ale
-----Messaggio originale-----
Da: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED]] Per conto di Paul Hiemstra
Inviato: martedì 2 dicembre 2008 12.07
A: Zev Ross
Cc: r-sig-geo@stat.math.ethz.ch
Oggetto: Re: [R-sig-Geo] GSTAT: Optimize power value for IDW
Zev Ross schreef:
Hi All,
ArcGIS has a nice little button that calculates the optimal power
value to use for inverse distance weighting based on cross-validation
and RMSPE. Just wondering if anyone had written something similar in R
-- I'm using GSTAT and I'd like to avoid back and forth with ArcGIS
(and obviously I'd like to avoid writing it myself as well!).
Zev
Hi,
I don't have any code lying around, but a brute force optimization
approach should be quite easy. Also because the range of idw-powers is
relatively small. The speed would ofcourse depend on the size of the
dataset. In code it would look something like (actually works :)):
library(gstat)
data(meuse)
coordinates(meuse) = ~x+y
# make function to do the cv
do_cv = function(idp) {
meuse_idw = gstat(id = "zn", formula = zinc~1, data = "" set =
list(idp = idp))
out = gstat.cv(meuse_idw)
return(sqrt(mean(out$residual^2))) # RMSE as optimization criterium
}
idw_pow = seq(0.1,5, by = 0.2) # the idwpower values that will be checked
cv_vals = sapply(idw_pow, do_cv) # calculate the rmse
# List of outcomes
print(data.frame(idp = idw_pow, cv_rmse = cv_vals))
After this you select the idw value with the smallest RMSE.
cheers and hth,
Paul
