R/cokm.condsim.R

In pulongma/ARCokrig: Autoregressive Cokriging Models for Multifidelity Codes

#' @title Conditional simulation at new inputs in the autoregressive cokriging model
#' @description This function simulate from predictive distributions in
#'  autogressive cokriging models
#'  
#' @param obj a \code{\link{cokm}} object construted via the function \code{\link{cokm}} in 
#' this package
#' @param input.new a matrix including new inputs for making prediction
#' @param nsample a numerical value indicating the number of samples
#' @author Pulong Ma <mpulong@gmail.com>
#' 
#' @seealso \code{\link{cokm}}, \code{\link{cokm.fit}}, \code{\link{cokm.predict}}, \code{\link{ARCokrig}}
#' @export
#' @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.condsim(obj, input.new, nsample=30)
#' 
#' 


cokm.condsim <- function(obj, input.new, nsample=30){

formula = obj@formula
output = obj@output 
input = obj@input 
param = obj@param
cov.model = obj@cov.model 
NestDesign = obj@NestDesign

phi = do.call(cbind, param)

if(!is(input.new, "matrix")){
  stop("input.new should be a matrix.")
}

if(NestDesign){
  pred.list = condsim.ND.univariate(formula=formula, output=output, input=input, 
              input.new=input.new, phi=phi, cov.model=cov.model, 
              nsample=nsample)
}else{
  pred.list = condsim.NN.univariate(formula=formula, output=output, input=input,
              input.new=input.new, phi=phi, cov.model=cov.model,
              nsample=nsample)
}


return(pred.list)


}