R/ce.4betaZINB.AIC.R

In breakpoint: An R Package for Multiple Break-Point Detection via the Cross-Entropy Method

ce.4betaZINB.AIC <-
function(N, data, h, L0, L, M, Melite, eps, a, r){
  
  if (N == 0){
    loglik.full <- loglikzinb(1, (L + 1), data, r, h)[[1]]
    AIC.full <- AICzinb(loglik.full, 0, L)
    return(list(loci = c(1, (L + 1)), AIC = AIC.full, LogLike = loglik.full))

  } else {
    
  ######################### Parameter initialization #####################
  new.para <- array(1, dim = c(2, N))
  ########################################################################
#  aic <- c()
  k <- 0
  
  repeat
  {
    k <- k + 1
    ch <- array(0, dim = c(M, (N + 2)))       
    ch[, 1] <- c(1)                    
    ch[, (N + 2)] <- c(L + 1)    
    ch[, (2 : (N + 1))] <- apply(new.para, 2, betarand, L0, L, M)   
    ch <- t(apply(ch, 1, sort))                              
    loglike <- apply(ch, 1, llhoodzinb, data, r, h)
    aic.vals <- apply(as.data.frame(loglike), 1, AICzinb, N, L) 
    ch <- cbind(ch, aic.vals, loglike)
    ch <- ch[order(ch[, (N + 3)], decreasing = FALSE), ]
    melitesmpl <- ch[1:Melite, ]                     
#    aic[k] <- melitesmpl[1, (N + 3)] 

    newpar.n <- array(0, dim = c(2, N))
    newpar.n[1, ] <- apply(as.matrix(melitesmpl[, (2 : (N + 1))]), 2, mean)
    newpar.n[2, ] <- apply(as.matrix(melitesmpl[, (2 : (N + 1))]), 2, var)   
    
    newpar.new <- array(0, dim = c(2, N))
    newpar.new[1, ] <- apply(newpar.n, 2, fun.alpha, L0, L)
    newpar.new[2, ] <- apply(newpar.n, 2, fun.beta, L0, L)
    new.para <- a * newpar.new + (1 - a) * new.para
    
    mad <- apply(as.matrix(melitesmpl[, (2 : (N + 1))]), 2, mad)
    
    if(max(mad) <= eps){break}
  }
    # return(list(loci = ch[1, (1 : (N + 2))], AIC = aic[k]))
  return(list(loci = ch[1, (1 : (N + 2))], AIC = melitesmpl[1, (N + 3)][[1]], LogLike = melitesmpl[1, (N + 4)][[1]]))
  }
}