HMMsim: HMMsim

In HMMmlselect: Determine the Number of States in Hidden Markov Models via Marginal Likelihood

Description

This function simulates HMM with the observed data being conditionally Gaussian distributed given the underlying state.

Usage

HMMsim(n, optionalsim = list())

Arguments

n	The length of HMM to be simulated.
optionalsim	Optional variables as a list. Possible options include:

• Ksim: Number of states of the simulated HMM. Default is 3.

• P: The transition matrix of the underlying Markov chain. Default is a flat K-by-K matrix.

• mu: The mean of the observed data given each underlying state. Default is {1, 2, ..., K}.

• sigma: The standard deviation of the observed data given each underlying state. Default is {0.1, 0.1, ... 0.1}.

• pi: The distribution of the initial state. Default is an uniform distribution across all possible states.

• BoolWritetoFile: Logic variable indicating whether to write the result into file or not. Default is FALSE.

• Filenameoutput: The output file name for the simulated HMM. Default is "HMMtrace.txt".

Details

See Manual.pdf in "inst/extdata" folder.

Value

It returns the sample of the simulated HMM.

References

Yang Chen, Cheng-Der Fuh, Chu-Lan Kao, and Samuel Kou (2019+) "Determine the number of states in hidden markov models via marginal likelihood." Submitted.

Examples

library(HMMmlselect)

# simulate a 25 observations HMM
obs = HMMsim ( n = 25 )$obs

# perform order selection and estimation
results = HMMmlselect ( y = obs, list(Kfits = c(2,3), boolUseMclust = FALSE) )

# visualize the results, see figure 1
PlotHMM ( y = obs, results )

HMMmlselect documentation built on May 3, 2020, 9:05 a.m.