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[3,] -1.381999e-02   (y)

[4,] -2.817476e-03   (x^2)

[5,] -9.021021e-04   (y^2)

[6,]  6.312104e-03   (x*y)

### kriging performed in moving neighbourhood:

coal.wind <-ksline(coords=coal.m[,2:3],data=coal.m[,4],locations=loci,

cov.pars=c(3,1),nugget=0,

cov.model="matern",

kappa=.5,m0="ok",nwin=5)

#nwin number of closest neighbors

coal.wind

 $predict:

 [1] 10.43828 10.84857

  $krige.var:

  [1] 3.136432 1.357889

###########################################3

# linear trend not subregions:

# when trend is one implies a linear trend in two dimensions

# therefore we have 3 parameters to estimate

coal.wind1 <-ksline(coords=coal.m[,2:3],data=coal.m[,4],locations=loci,

cov.pars=c(3,1),nugget=0,

cov.model="matern",

kappa=.5,m0="kt",trend=1,nwin="full")

> coal.wind1

coal.wind1$predict

[1] 10.62524 11.23732

coal.wind1$krige.var

[1] 2.774784 1.349341

coal.wind1$beta

            [,1]

[1,]  10.86216801

[2,]  -0.16286289

[3,]   0.01077067

### kriging performed in moving neighborhood with linear trend

coal.wind2 <-ksline(coords=coal.m[,2:3],data=coal.m[,4],locations=loci,

cov.pars=c(3,1),nugget=0,

cov.model="matern",

kappa=.5,m0="kt",trend=1,nwin=5)

coal.wind2

coal.wind2$predict

[1]  9.669875 10.876154

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