[R-sig-Geo] regression-kriging and co-kriging

[Emanuele Barca]

Dear Edzer,

maybe I found the solution. I found this in the predict function help: 
"When a non-stationary (i.e., non-constant) mean is used, both for 
simulation and prediction purposes the variogram model defined should be 
that of the residual process, and not that of the raw observations"
Since my data were, actually, non-stationary, I applied the universal 
co-kriging instead usual co-kriging.
now the maps of regression-kring and co-kriging are actually similar s 
expected.
did I understand correctly the quoted sentence?

regards

emanuele barca
------------------------------
> 
> Message: 2
> Date: Sat, 10 Aug 2019 10:41:38 +0200
> From: Edzer Pebesma <edzer.pebesma using uni-muenster.de>
> To: r-sig-geo using r-project.org
> Subject: Re: [R-sig-Geo] regression-kriging and co-kriging
> Message-ID: <da76da51-e46b-8a8d-4952-7c3b85c19687 using uni-muenster.de>
> Content-Type: text/plain; charset="utf-8"
> 
> Hard to tell from your script. Maybe give a reproducible example?
> 
> On 8/6/19 1:07 PM, Emanuele Barca wrote:
>> Dear  r-sig-geo friends,
>> 
>> I produced two maps garnered in the following way:
>> 
>> # for regression-kriging
>> Piezo.map <-autoKrige(LivStat ~  Z, input_data = mydata.sp, new_data
>> = covariates,  model = "Ste")
>> 
>> Piezork.pred <- Piezo.map$krige_output$var1.pred
>> Piezork.coords <- Piezo.map$krige_output using coords
>> Piezork.out <- as.data.frame(cbind(Piezork.coords, Piezork.pred))
>> colnames(Piezork.out)[1:2] <- c("X", "Y")
>> coordinates(Piezork.out) = ~ X + Y
>> gridded(Piezork.out) <- TRUE
>> 
>> spplot(Piezork.out, main = list(label = "R-k Hydraulic head", cex = 
>> 1.5))
>> 
>> #for co-kriging
>> g <- gstat(id = "Piezo", formula = LivStat ~ 1, data = mydata.sp, set
>> = list(nocheck = 1))
>> g <- gstat(g, id = "Z", formula = Z ~ 1, data = mydata.sp, set =
>> list(nocheck = 1))
>> 
>> v.g <- variogram(g)
>> 
>> #g <- gstat(g, id = "Piezo", model = vgm(150, "Mat", 1350, 0.0, kappa
>> = 1.9), fill.all = T)#
>> g <- gstat(g, id = "Piezo", model = vgm(0.7, "Ste", 1300, 18, kappa =
>> 1.9), fill.all = T)#
>> g.fit <- fit.lmc(v.g, g, fit.lmc = TRUE, correct.diagonal = 1.01) #
>> fit multivariable variogram model , fit.lmc = TRUE, correct.diagonal = 
>> 1.01
>> g.fit
>> plot(v.g, model = g.fit, main = "Fitted Variogram Models - Raw Data")#
>> #gridded(covariates) <- TRUE
>> g.cok <- predict(g.fit, newdata = covariates)#grid
>> 
>> g.cok.pred <- g.cok using data$Piezo.pred
>> aaaa <- na.omit(g.cok.pred)
>> g.cok.coords <- g.cok using coords
>> g.cok.out <- as.data.frame(cbind(g.cok.coords, g.cok.pred))
>> colnames(g.cok.out)[1:2] <- c("X", "Y")
>> coordinates(g.cok.out) = ~ X + Y
>> gridded(g.cok.out) <- TRUE
>> spplot(g.cok.out, main = list(label = "Hydraulic head with
>> Co-kriging", cex = 1.5))
>> 
>> ###########################################################################################################################
>> 
>> I am unable to understand why the first map appears as a raster and
>> the second not, notwithstanding the fact that they are both computed
>> on the same "covariates" DEM???
>> 
>> where is the mistake???
>> 
>> regards
>> 
>> emanuele
>> 
>> ________________________________________________________
>> Emanuele Barca                               Researcher
>> Water Research Institute                       (IRSA-CNR)
>> Via De Blasio, 5                       70123 Bari (Italy)
>> Phone +39 080 5820535               Fax  +39 080 5313365
>> Mobile +39 340 3420689
>> _________________________________________________________
>> 
>> 
>> 
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> 
> --
> Edzer Pebesma
> Institute for Geoinformatics
> Heisenbergstrasse 2, 48151 Muenster, Germany
> Phone: +49 251 8333081
> 
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