sim.hmm0norm: Simulation of a 1-D HMM with Extra Zeros In HMMextra0s: Hidden Markov Models with Extra Zeros

Description

Simulates the observed process and the associated binary variable of a 1-D HMM with extra zeros.

Usage

 1 sim.hmm0norm(mu, sig, pie, gamma, delta, nsim = 1, seed = NULL)

Arguments

 pie is a vector of length m, the jth element of which is the probability of Z=1 when the process is in state j. gamma is the transition probability matrix (m * m) of the hidden Markov chain. mu is a 1 * m matrix, the jth element of which is the mean of the (Gaussian) distribution of the observations in state j. sig is a 1 * m matrix, the jth element of which is the standard deviation of the (Gaussian) distribution of the observations in state j. delta is a vector of length m, the initial distribution vector of the Markov chain. nsim is an integer, the number of observations to simulate. seed is the seed for simulation. Default seed=NULL.

Value

 x is the simulated observed process. z is the simulated binary data with the value 1 indicating that an event was observed and 0 otherwise. mcy is the simulated hidden Markov chain.

Ting Wang

References

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.

Examples

 1 2 3 4 5 6 7 8 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)

HMMextra0s documentation built on Aug. 3, 2021, 9:06 a.m.