Hi Moshood,
For an introduction to cross-validation I would recommend "An
Introduction to Statistical Learning, with Applications in R" (James,
Witten, Hastie, Tibshirani - Springer 2013), section 5.1.
This textbook is available for free online at
http://www-bcf.usc.edu/~gareth/ISL/.
El 15/05/2014 17:39, Moshood Agba Bakare escribió:
> Hi Michele,
> I have similar problem. I used ordinary kriging and inverse distance
> weighting method (IDW) to generate set of interpolated values from the
> same interpolation grid. I don't understand how cross validation can
> be done to come up with diagnostic statistics such as mse, rmse to use
> as basis for identifying the best interpolation method. I used
> krige.cv <http://krige.cv> but I encountered error message. Please any
> advice on what to do please?
>
> ## Create grid for the interpolation through ordinary kriging and idw
>
> 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
>
> prok <- krige(id="yield",yield ~ 1, canmod.sp, newdata = grid,
> model=exp.mod,nmax=20,maxdist=33.0)
>
> ## Inverse Distance Weighting (IDW) Interpolation method
>
> yield.idw = idw(yield~1, canmod.sp, grid,nmax=20,maxdist=33.0,idp=1)
>
>
> Thanks
> Moshood
>
>
>
> On Thu, May 15, 2014 at 9:23 AM, Michele Fiori
> <mfi...@arpa.sardegna.it <mailto:mfi...@arpa.sardegna.it>> wrote:
>
> Thank you for your kind reply
> therefore as I have used the Osl method for regression, my result
> will never
> match the universal kriging; However, in order to validate my
> method, I'm
> trying to implement in the script a calculation loop witch runs n
> times (the
> number of stations) regression + kriging without one station at a
> time.
> Thank you again
> Michele
>
>
> -----Messaggio originale-----
> Da: r-sig-geo-boun...@r-project.org
> <mailto:r-sig-geo-boun...@r-project.org>
> [mailto:r-sig-geo-boun...@r-project.org
> <mailto:r-sig-geo-boun...@r-project.org>]
> Per conto di rubenfcasal
> Inviato: martedì 29 aprile 2014 19:49
> A: r-sig-geo@r-project.org <mailto:r-sig-geo@r-project.org>
> Oggetto: Re: [R-sig-Geo] Regression Kriging cross validation
>
> Hello Michele,
>
> Universal kriging is equivalent to Linear Regression (with the
> generalized-least-squaresestimator) + Simple Kriging of residuals
> (e.g.
> Cressie, 1993, section 3.4.5). The differences you observe are
> probably due
> to the use of ordinary least squares. If you use (leave-one-out)
> cross-validation with krige.cv <http://krige.cv> (considering the
> UK model), the trend is also
> re-estimated at each prediction location. From my point of view,
> this would
> be the recommended way to proceed.
> As far as I know, there are no available implementations of the
> procedure you are suggesting.
>
> Best regards,
> Rubén.
>
>
> El 29/04/2014 13:33, Michele Fiori escribió:
> > Hi everyone,
> > I am working on rainfall interpolation using regression kriging
> method
> > and I need suggestions on how I can carry out a cross validation
> > (leave-one-out) for this elaboration. At first I tried to apply
> > directly Krige.cv, similarly to UK method (example for october:
> > PP10uk.cv <- krige.cv <http://krige.cv>(reg, prec2, PP10.vgm)),
> but unfortunately when I
> > applied Universal Kriging on the same data, I realized that UK
> map was a
> little different from RK map.
> > So my question is: How could I manage universal kriging in order to
> > make it equivalent to regression kriging and use the above
> > cross-validation, or is there another different method to apply
> cross
> > validation (leave-one-out) on Regression Kriging interpolation?
> > Below my code:
> > Many thanks
> >
> > Michele Fiori
> >
> > ARPAS - Environmental Protection Agency of Sardinia MeteoClimatic
> > Department - Meteorological Service
> >
> > Viale Porto Torres 119 - 07100 Sassari, Italy Tel + 39 079
> 258617 <tel:%2B%2039%20079%20258617> Fax
> > + 39 079 262681 <tel:%2B%2039%20079%20262681>
> www.sardegnaambiente.it/arpas <http://www.sardegnaambiente.it/arpas>
> >
> > #### Creating SpatialPixelDataFrame ("dem" - 250x250 m grid)
> > ....
> > #### Loading Precipitation data
> > prec2 <- read.table("prec2.txt", sep="\t", header =TRUE)
> > coordinates(prec2) <- c("x", "y")
> > proj4string(prec2) <- CRS("+init=epsg:32632")
> > #### Linear regression Model
> > mod.gen <- lm(PP10 ~ QUOTA_MARE + UTM_EST + UTM_NORD +
> DIST_MARE,
> > prec2)
> > step1 <- stepAIC(mod.gen, direction="both")
> > reg <- formula(step1)
> > PP10.lm <- lm(reg, prec2)
> > summary(PP10.lm)
> > prec2$residuals <- residuals(PP10.lm)
> > dem$predlm <- predict(PP10.lm, dem)
> > #### Variogram of residuals
> > PP10.vgm <- vgm(nugget=51.46, model="Sph", range=38038.89,
> > psill=86.44)
> > #### Ordinary Kriging of residuals
> > PP10.okr <- krige(PP10.lm$residuals ~ 1, prec2, dem, PP10.vgm,
> > maxdist=Inf)
> > dem$varokr <- PP10.okr$var1.pred
> > #### Regression Kriging (Linear Regression + Ordinary Kriging of
> > residuals)
> > dem$vark <- dem$predlm + dem$varokr
> > #### Universal kriging
> > PP10.uk <- krige(reg, prec2, dem, PP10.vgm, maxdist=Inf)
> > dem$varuk <- PP10.uk$var1.pred
> > dem$difference <- dem$vark - dem$varuk
> > spplot(dem[c("difference")], col.regions=terrain.colors(25),
> > contour=FALSE, cuts = 15)
> >
> > _______________________________________________
> > R-sig-Geo mailing list
> > R-sig-Geo@r-project.org <mailto:R-sig-Geo@r-project.org>
> > https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> >
>
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