cokm.predict {ARCokrig}

Prediction at new inputs in the autoregressive cokriging model

Description

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.

Usage

cokm.predict(obj, input.new)

Arguments

obj

a cokm object construted via the function cokm in this package

input.new

a matrix including new inputs for making prediction

Author(s)

Pulong Ma <[e-mail]>

See Also

cokm, cokm.fit, cokm.condsim, ARCokrig

Examples

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)