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############################################################

##EXERCISE 2: Moving neighborhood  kriging

###"Kriging performed in global neighborhood":

#      cov.pars : covariance parameters vector (partial sill,range)

#      m0 : defines the type of kriging:

#                                 'sk': simple kriging (no trend)

#                                'ok': ordinary kriging (constant trend)

#                                'kt': kriging with a trend model(universal)

#      kappa : kappa (smoothing) is the smoothing

# parameter for Matern or powered exponential covariance function

coal.k<-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")

##coal.k

 coal.k$predict

 [1] 10.07305 11.22320

 coal.k$krige.var

 [1] 2.684456 1.349229

 coal.k$beta

        [,1]

 [1,] 9.752618

  coal.k$message:

 [1] "Kriging performed in global neighborhood"

##Universal Kriging:

#

# the value of trend here is 2 which means we have a polynomial

# of degree 2 for a two dimensional problem, therefore

# we need to estimate 5 parameters

coal.uk<-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=2)

coal.uk$predict

[1] 10.77988 11.24153

coal.uk$krige.var

[1] 3.023352 1.349461

coal.uk$beta

     coefficients:

[1,]  1.124853e+01   (1)

[2,] -2.090557e-01   (x)

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