date:20190815

Re: [R-sig-Geo] regression-kriging and co-kriging (Edzer Pebesma)

2019-08-15 Thread Edzer Pebesma

gt; = 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@data$Piezo.pred
>>>>>  <- na.omit(g.cok.pred)
>>>>> g.cok.coords <- g.cok@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
>>>>> _
>>>>>
>>>>>
>>>>>
>>>>> ---
>>>>> Questa e-mail è stata controllata per individuare virus con Avast
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>>>>> https://www.avast.com/antivirus
>>>>>
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>>>>>
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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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Edzer Pebesma
Institute for Geoinformatics
Heisenbergstrasse 2, 48151 Muenster, Germany
Phone: +49 251 8333081


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Re: [R-sig-Geo] regression-kriging and co-kriging (Edzer Pebesma)

2019-08-15 Thread Emanuele Barca

--

Message: 2
Date: Thu, 15 Aug 2019 08:33:59 +0200
From: Edzer Pebesma 
To: r-sig-geo@r-project.org
Subject: Re: [R-sig-Geo] regression-kriging and co-kriging
Message-ID: 
Content-Type: text/plain; charset="utf-8"



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 
To: r-sig-geo@r-project.org
Subject: Re: [R-sig-Geo] regression-kriging and co-kriging
Message-ID: 
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@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@data$Piezo.pred
 <- na.omit(g.cok.pred)
g.cok.coords <- g.cok@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

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

2019-08-15 Thread 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 
>> To: r-sig-geo@r-project.org
>> Subject: Re: [R-sig-Geo] regression-kriging and co-kriging
>> Message-ID: 
>> 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$krige_output@coords
>>> Piezork.out <- as.data.frame(cbind(Piezork.coords, Piezork.pred))
>>> colnames(Piezork.out)[1:2] <- c("X", "Y")
>>> coordinates(Piezork.out) = ~ X + Y
>>> gridded(Piezork.out) <- TRUE
>>>
>>> spplot(Piezork.out, main = list(label = "R-k Hydraulic head", cex =
>>> 1.5))
>>>
>>> #for co-kriging
>>> g <- gstat(id = "Piezo", formula = LivStat ~ 1, data = mydata.sp, set
>>> = list(nocheck = 1))
>>> g <- gstat(g, id = "Z", formula = Z ~ 1, data = mydata.sp, set =
>>> list(nocheck = 1))
>>>
>>> v.g <- variogram(g)
>>>
>>> #g <- gstat(g, id = "Piezo", model = vgm(150, "Mat", 1350, 0.0, kappa
>>> = 1.9), fill.all = T)#
>>> g <- gstat(g, id = "Piezo", model = vgm(0.7, "Ste", 1300, 18, kappa =
>>> 1.9), fill.all = T)#
>>> g.fit <- fit.lmc(v.g, g, fit.lmc = TRUE, correct.diagonal = 1.01) #
>>> fit multivariable variogram model , fit.lmc = TRUE, correct.diagonal
>>> = 1.01
>>> g.fit
>>> plot(v.g, model = g.fit, main = "Fitted Variogram Models - Raw Data")#
>>> #gridded(covariates) <- TRUE
>>> g.cok <- predict(g.fit, newdata = covariates)#grid
>>>
>>> g.cok.pred <- g.cok@data$Piezo.pred
>>>  <- na.omit(g.cok.pred)
>>> g.cok.coords <- g.cok@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
>>> _
>>>
>>>
>>>
>>> ---
>>> Questa e-mail è stata controllata per individuare virus con Avast
>>> antivirus.
>>> https://www.avast.com/antivirus
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> ___
>>> R-sig-Geo mailing list
>>> R-sig-Geo@r-project.org
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>>
>>
>> -- 
>> 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

Re: [R-sig-Geo] regression-kriging and co-kriging (Edzer Pebesma)

Re: [R-sig-Geo] regression-kriging and co-kriging (Edzer Pebesma)

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

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