R/complk_cont.R

In LMest: Generalized Latent Markov Models

complk_cont <- function(Y,yv,piv,Pi,Mu,Si,k){

# Preliminaries
  sY = dim(Y)
  ns = sY[1]
  TT = sY[2]
  n = sum(yv)
  if(length(sY)==2) r = 1 else r = sY[3]
  if(r==1){
    if(is.matrix(Y)) Y = array(Y,c(dim(Y),1))
#  		if(is.matrix(R)) R = array(R,c(dim(R),1))
  }

# Compute log-likelihood
  if(k==1){
    pv = dmvnorm(matrix(Y[,1,],ns,r),Mu,Si)
    for(t in 2:TT) pv = pv*dmvnorm(matrix(Y[,t,],ns,r),Mu,Si)
    Phi = L = NULL
  }else{
    Phi = array(1,c(ns,k,TT)); L = array(0,c(ns,k,TT))
    for(u in 1:k) Phi[,u,1] = dmvnorm(matrix(Y[,1,],ns,r),Mu[,u],Si)
    L[,,1] = Phi[,,1]%*%diag(piv)
    for(t in 2:TT){
      for(u in 1:k) Phi[,u,t] =  dmvnorm(matrix(Y[,t,],ns,r),Mu[,u],Si)
      L[,,t] = Phi[,,t]*(L[,,t-1]%*%Pi[,,t])
    }
    if(n==1) pv = sum(L[1,,TT]) else pv = rowSums(L[,,TT])
  }
  lk = sum(yv*log(pv))
  out = list(lk=lk,Phi=Phi,L=L,pv=pv)

}