[R-sig-Geo] R: gstat cross validation for accuracy ordinary kriging vs IDW

[Michele Fiori]

Hi Moshood, 
I think you can do this way

#### Ordinary kriging to create kriging prediction orkrig <- krige(yield ~
1, canmod.sp, newdata = grid, model=exp.mod,nmax=20)

		OrdKrigecv  <- Krige.cv(yield ~ 1, canmod.sp,
model=exp.mod,nmax=20)
 		RMSE.ok <- sqrt(sum(OrdKrigecv
$residual^2)/length(OrdKrigecv $residual))

## Inverse Distance Weighting (IDW) Interpolation method maxdist=16.5

      		idw1cv <- krige.cv(yield~1, canmod.sp, nmax=20,idp=1))
	      	RMSE.id <- sqrt(sum(idw1cv $residual^2)/length(idw1cv
$residual))

-----Messaggio originale-----
Da: r-sig-geo-bounces at r-project.org [mailto:r-sig-geo-bounces at r-project.org]
Per conto di Moshood Agba Bakare
Inviato: mercoledì 21 maggio 2014 21:16
A: r-sig-geo at r-project.org
Oggetto: [R-sig-Geo] gstat cross validation for accuracy ordinary kriging vs
IDW

Hi all,

I have been having a couple of challenge with my analysis. I have
irregularly space spatial yield monitor data over four years. Pooling this
data together is not feasible because of misalignment. That is, the
coordinates of data point varies from one year to the other.
I  created a common regular interpolation grid for each year with the same
grid size of 10 x 10 m. I am able to get interpolated value for each point
using ordinary kriging and inverse distance weighting method (IDW). Please
how I cross validate this two interpolation methods to know which one give
me the best estimate.

The problem I notice is that there is no observe value in each interpolation
point to assess the prediction accuracy of these methods.
Please what do I do? see my script below. I correlated the interpolated
values from the two methods. They are highly correlated (r=0.98). How do I
know which  method gave good prediction?

grid <- expand.grid(easting=seq(from = 299678, to = 301299, by=10),
                    northing=seq(from = 5737278, to = 5738129, by=10))

## convert the grid to SpatialPixel class to indicate gridded spatial data
coordinates(grid)<-~easting+northing
proj4string(grid)<-CRS("+proj=utm +zone=12 +ellps=WGS84 +datum=WGS84
+units=m +no_defs +towgs84=0,0,0")

gridded(grid)<- TRUE


#### Ordinary kriging to create kriging prediction orkrig <- krige(yield ~
1, canmod.sp, newdata = grid, model=exp.mod,nmax=20)



## Inverse Distance Weighting (IDW) Interpolation method maxdist=16.5
idw1 = idw(yield~1, canmod.sp, newdata=grid,nmax=20,idp=1)

Thanks while looking forward to reading from you.

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