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

[Edzer Pebesma]

On 8/12/19 8:21 PM, Emanuele Barca wrote:
> 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?

I think so, but hard to be sure given the information you provide.

> 
> 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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-- 
Edzer Pebesma
Institute for Geoinformatics
Heisenbergstrasse 2, 48151 Muenster, Germany
Phone: +49 251 8333081

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