R/draw_lm_basic.R

In LMest: Generalized Latent Markov Models

draw_lm_basic <-
function(piv,Pi,Psi,n){

#        [lk,piv,Pi,Psi,np,aic,bic,lkv] = draw_lm_basic(piv,Pi,Psi,n)
#
# Draw a sample of size n from a Basic Latent Markov model with parameter piv, Pi and Psi
  warning("draw_lm_basic function is no longer maintained. Please look at drawLMbasic function",call. = FALSE)

  k = length(piv)
  dd = dim(Psi)
  c = apply(Psi, c(2,3), function(x) sum(!is.na(x)))[1,]
  TT = dim(Pi)[3]
  if (length(dd) > 2)
    r = dd[3]
  else r = 1
  Y = matrix(0, n, TT * r)
  cat("------------|\n")
  cat(" sample unit|\n")
  cat("------------|\n")
  for (i in 1:n) {
    if (i/1000 == floor(i/1000))
      cat(sprintf("%11g", i), "\n", sep = " | ")
    u = k + 1 - sum(runif(1) < cumsum(piv))
    ind = 0
    for (j in 1:r) {
      ind = ind + 1
      Y[i, ind] = c[j] - sum(runif(1) < cumsum(Psi[, u, j]), na.rm=T)
    }
    for (t in 2:TT) {
      u = k + 1 - sum(runif(1) < cumsum(Pi[u, , t]))
      for (j in 1:r) {
        ind = ind + 1
        Y[i, ind] = c[j] - sum(runif(1) < cumsum(Psi[,
                                                     u, j]), na.rm=T)
      }
    }
  }
  cat("------------|\n")
  out = aggr_data(Y)
  S = out$data_dis
  yv = out$freq
  S = array(t(S), c(r, TT, length(yv)))
  S = aperm(S)
  if (r == 1)
    S = S[, , 1]
  out = list(Y = Y, S = S, yv = yv)

  return(out)
}