Description Usage Arguments Value Author(s) References Examples

Calculates the parameter estimates of a 1-D HMM with observations having extra zeros.

1 |

`R` |
is the observed data. |

`Z` |
is the binary data with the value 1 indicating that an event was observed and 0 otherwise. |

`pie` |
is a vector of length |

`gamma` |
is the transition probability matrix ( |

`mu` |
is a |

`sig` |
is a |

`delta` |
is a vector of length |

`tol` |
is the tolerance for testing convergence of the iterative estimation process. The default tolerance is 1e-6. For initial test of model fit to your data, a larger tolerance (e.g., 1e-3) should be used to save time. |

`print.level` |
controls the amount of output being printed. Default is 1. If |

`fortran` |
is logical, and determines whether Fortran code is used; default is |

`pie` |
is the estimated probability of |

`mu` |
is the estimated mean of the (Gaussian) distribution of the observations in each state. |

`sig` |
is the estimated standard deviation of the (Gaussian) distribution of the observations in each state. |

`gamma` |
is the estimated transition probability matrix of the hidden Markov chain. |

`delta` |
is the estimated initial distribution vector of the Markov chain. |

`LL` |
is the log likelihood. |

Ting Wang

Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (2016) Hidden Markov Modeling of Sparse Time Series from Non-volcanic Tremor Observations. Journal of the Royal Statistical Society, Series C, Applied Statistics, 66, Part 4, 691-715.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
pie <- c(0.002,0.2,0.4)
gamma <- matrix(c(0.99,0.007,0.003,
0.02,0.97,0.01,
0.04,0.01,0.95),byrow=TRUE, nrow=3)
mu <- matrix(c(0.3,0.7,0.2),nrow=1)
sig <- matrix(c(0.2,0.1,0.1),nrow=1)
delta <- c(1,0,0)
y <- sim.hmm0norm(mu,sig,pie,gamma,delta, nsim=5000)
R <- as.matrix(y$x,ncol=1)
Z <- y$z
yn <- hmm0norm(R, Z, pie, gamma, mu, sig, delta)
yn
``` |

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