[R-sig-Geo] kriging and cokriging with multiple covariate

[TM]

Dear Andrew,

Thank you very much

Toni


----- Original Message ----- 
From: "Andrew Finley" <finleya at msu.edu>
To: "TM" <amonleong at wanadoo.es>
Cc: <r-sig-geo at r-project.org>
Sent: Tuesday, February 08, 2011 1:09 PM
Subject: Re: [R-sig-Geo] kriging and cokriging with multiple covariate


> Hi Toni,
> Check out the spMvLM function in the spBayes package (or spMvGLM 
> depending on your outcome variable), there are also some illustrative 
> slides and examples toward the bottom of this page 
> http://blue.for.msu.edu/JBC_10/SC .
> Let me know if you have any questions-
> Andy
>
> Quoting TM <amonleong at wanadoo.es>:
>
>> Hi all,
>>
>> I'm trying to model some ecological data of an area to predict  spatially 
>> some ecological response variable  in terms of x, y, and  some covariates 
>> have been measured. The problem is that some of the  covariates are 
>> discrete (ordinal) and do not really know how to make  a cokriging with 
>> multiple covariates.
>>
>> Bayesian cokriging or perhaps a transformation based on a mixed  distance 
>> as the Gower?
>>
>> Any idea? I add below a R code with some of the data, the covariates  and 
>> the response variable.
>>
>> How  can I do a Cross-validation with used data  to determine  whether 
>> the estimated model fits well?
>>
>> Thank you very much in advance.
>>
>> Toni
>> Department of Statistics
>> University of Barcelona
>>
>> ###########################################################################################################
>> ############################         kriging and cokriging  with 
>> multiple covariate      ##########################################
>> ###########################################################################################################
>> ###########################################################################################################
>> ###########################################################################################################
>>
>> data_example<-structure(list(x = c(430234, 429626, 413104, 456047, 
>> 475226, 422256, 446443, 456281, 433773, 404269),
>> y = c(4588887, 4579902, 4574398, 4621071, 4610062, 4587858, 4609701, 
>> 4617064, 4600892, 4575527, 4587229, 4620543),
>> var1 =c(1287, 131, 91, 21, 5, 311, 22, 57, 1252, 113),
>> var2 =c(39.26, 68.13, 36.42, 16.06, 46.75, 34.19, 56.87, 31.04,  35.19, 
>> 15.40),
>> var3 =c(3, 9, 1, 1, 2, 1, 10, 2, 11, 1),
>> var4 =c(1, 3, 1, 1, 1, 1, 3, 1, 1, 1),
>> var5 =c(0.41, 0.89, 0.77, 0.29, 0.93, 0.31, 0.94, 0.84, 0.80, 0.60),
>> var6 =c(4.02, 8.63, 6.08, 2.60, 8.64, 3.81, 7.65, 5.81, 4.31, 2.13),
>> var7 =c(1.07, 1.12, 1.08, 1.10, 1.07, 1.15, 1.08, 1.07, 1.08, 1.05)),
>> .Names = c("x", "y", "Cov1", "Cov2",  "Cov3", "Cov4", "Cov5", 
>> "Response", "Cov6" ), class = "data.frame", row.names = c("1", "2",  "3", 
>> "4", "5", "6", "7", "8", "9", "10"))
>>
>>
>> library(gstat)
>> data(data_example)
>> attach(data_example)
>>
>> # Ordinary Kriging in gstat
>>
>> # compute experimental variogram
>> v <- variogram(response~1, ~ x+y, data_example)
>> # estimated model
>> m <- vgm(1.4, "Sph", 12000, 0.5)
>> # fitted model
>> m.f <- fit.variogram(v, m)
>>
>> new.locs <- data.frame(
>> x = c(430334, 429726, 413204, 456147, 475326, 422356, 446543,  456381, 
>> 433873, 404369, 422732),
>>  y = c(4588987, 4571002, 4574498, 4621171, 4610162, 4587958,  4609801, 
>> 4617164, 4600992, 4575627, 4587329))
>>
>> kr <- krige(response~ 1, loc=~ x+y, data=data_example,  newdata=new.locs, 
>> model=m.f)
>>
>>
>> library (lattice)
>> # visualize interpolation; note aspect option to get correct geometry
>> levelplot(var1.pred ~ x+y, kr)
>> # visualize prediction error
>> levelplot(var1.var ~ x+y, data=kr,col.regions=terrain.colors(40))
>>
>>
>> [[alternative HTML version deleted]]
>>
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>>
>
>
>