Description Usage Arguments Value Author(s) References Examples

View source: R/Viterbi.hmm0norm.R

Finds the most probable sequence of hidden states of an observed process.

1 | ```
Viterbi.hmm0norm(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 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., 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
HMMEST <- hmm0norm(R, Z, pie, gamma, mu, sig, delta)
Viterbi3 <- Viterbi.hmm0norm(R,Z,HMMEST)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.