Description Usage Arguments Author(s) See Also Examples
This function makes prediction in autogressive cokriging models. If a nested design is used, the predictive mean and predictive variance are computed exactly; otherwise, Monte Carlo simulation from the predictive distribution is used to approximate the predictive mean and predictive variance.
1 | cokm.predict(obj, input.new)
|
obj |
a |
input.new |
a matrix including new inputs for making prediction |
Pulong Ma <mpulong@gmail.com>
cokm
, cokm.fit
, cokm.condsim
, ARCokrig
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | Funcc = function(x){
return(0.5*(6*x-2)^2*sin(12*x-4)+10*(x-0.5)-5)
}
Funcf = function(x){
z1 = Funcc(x)
z2 = 2*z1-20*x+20 + sin(10*cos(5*x))
return(z2)
}
#####################################################################
###### Nested design
#####################################################################
Dc <- seq(-1,1,0.1)
indDf <- c(1, 3, 6, 8, 10, 13, 17, 21)
zc <- Funcc(Dc)
Df <- Dc[indDf]
zf <- Funcf(Df)
input.new = as.matrix(seq(-1,1,length.out=200))
## create the cokm object
prior = list(name="Reference")
obj = cokm(formula=list(~1,~1+x1), output=list(c(zc), c(zf)),
input=list(as.matrix(Dc), as.matrix(Df)),
prior=prior, cov.model="matern_5_2")
## update model parameters in the cokm object
obj = cokm.fit(obj)
cokrige = cokm.predict(obj, input.new)
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