R/meanFunc2.R

In ick003/SpTMixture: Mixture models with spatio-temporal dependencies

#' Another mean function for EM
#'
#' Another mean function for EM
#' @param theta parameters to be estimated
#' @param n.mixt number of mixture.
#' @param p.sites the composition of each site.
#' @keywords mean function
#' @export
#' @examples
#' data("wq_analysis_week2")
#' SPTMData(wq.raw.obs, frequency = "quarter")
meanFunc2 = function(theta, n.mixt = 4, p.sites, season.lu, trend.lu){

  cms = matrix(theta, ncol = n.mixt, byrow = T)

  # Method 1

  Fint = kronecker(diag(n.mixt),rep(1, nrow(season.lu)))
  Fs = kronecker(diag(n.mixt),season.lu[,1])
  Ft = kronecker(diag(n.mixt),trend.lu[,1])
  idx.F = 1:nrow(season.lu)
  for(i in 2:n.mixt){
    idx.F = seq_len(nrow(season.lu)) + max(idx.F)
    Fs[idx.F,i] = season.lu[,i]
    Ft[idx.F,i] = trend.lu[,i]
  }

  mF = matrix(Fint %*% matrix(cms[1,], ncol=1), ncol = n.mixt)
  tF = matrix(Ft %*% matrix(cms[2,], ncol=1), ncol = n.mixt)
  sF = matrix(Fs %*% matrix(cms[3,], ncol=1), ncol = n.mixt)

  F = cbind(Fint, Ft, Fs)

  res = matrix(F %*% theta[1:(length(theta)-n.mixt)], ncol = n.mixt)

  return(res)
}