Description Usage Arguments Value References Examples
Estimates the parameters of a hidden Markov model using maximum penalized likelihood estimation. For details, see Adam et al. (2019).
1 2 |
x |
Vector containing the observed time series of counts. |
N |
Integer, number of states. Default is |
probs0 |
Matrix with |
gamma0 |
Initial parameter values for the transition probabilities of the Markov chain underlying the observed counts. Matrix with |
delta0 |
Initial parameter values for the initial probabilities of the Markov chain underlying the observed counts. Vector of length |
stationary |
Logical, determines whether the initial distribution of the Markov chain underlying the observed counts is the stationary distribution. Default is |
lambda |
Vector of length |
sup |
Integer, determines the upper bound of the support of the state-dependent distributions. If |
m |
Integer, order of the difference penalties. Default is |
inflation |
Count probabilities to be excluded from penalization (e.g. in the presence of zero-inflation). Default is |
An object of type countHMM.
Adam, T., Langrock, R., and Wei<c3><9f>, C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. arXiv:https://arxiv.org/pdf/1901.03275.pdf.
1 2 3 4 5 | # importing example data
x = read.table("http://www.hmms-for-time-series.de/second/data/earthquakes.txt")$V2
# model fitting
lambda = rep(10^4,2)
fitMod(x=x,lambda=lambda)
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