Description Usage Arguments Value Author(s) References Examples

View source: R/Viterbi.hmm0norm2d.R

Finds the most probable sequence of hidden states of an observed process of a bivariate HMM with extra zeros.

1 | ```
Viterbi.hmm0norm2d(R, Z, HMMest)
``` |

`R` |
is the observed data. |

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

`HMMest` |
is a list which contains pie, gamma, sig, mu, and delta (the bivariate HMM parameter estimates). |

`y` |
is the estimated Viterbi path. |

`v` |
is the estimated probability of each time point being in each state. |

Ting Wang

Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) Identifying the recurrence patterns of non-volcanic tremors using a 2D hidden Markov model with extra zeros. Journal of Geophysical Research, doi: 10.1029/2017JB015360.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
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(35.03,137.01,
35.01,137.29,
35.15,137.39),byrow=TRUE,nrow=3)
sig <- array(NA,dim=c(2,2,3))
sig[,,1] <- matrix(c(0.005, -0.001,
-0.001,0.01),byrow=TRUE,nrow=2)
sig[,,2] <- matrix(c(0.0007,-0.0002,
-0.0002,0.0006),byrow=TRUE,nrow=2)
sig[,,3] <- matrix(c(0.002,0.0018,
0.0018,0.003),byrow=TRUE,nrow=2)
delta <- c(1,0,0)
y <- sim.hmm0norm2d(mu,sig,pie,gamma,delta, nsim=5000)
R <- y$x
Z <- y$z
HMMEST <- hmm0norm2d(R, Z, pie, gamma, mu, sig, delta)
Viterbi3 <- Viterbi.hmm0norm2d(R,Z,HMMEST)
``` |

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