Description Usage Arguments Details Value See Also Examples
emMixHMM implements the maximum-likelihood parameter estimation of a mixture of HMM models by the Expectation-Maximization (EM) algorithm, known as Baum-Welch algorithm in the context of mixHMM.
1 2 3 |
Y |
Matrix of size (n, m) representing the observed
responses/outputs. |
K |
The number of clusters (Number of HMM models). |
R |
The number of regimes (HMM components) for each cluster. |
variance_type |
Optional character indicating if the model is "homoskedastic" or "heteroskedastic". By default the model is "heteroskedastic". |
order_constraint |
Optional. A logical indicating whether or not a mask
of order one should be applied to the transition matrix of the Markov chain
to provide ordered states. For the purpose of segmentation, it must be set
to |
init_kmeans |
Optional. A logical indicating whether or not the curve partition should be initialized by the K-means algorithm. Otherwise the curve partition is initialized randomly. |
n_tries |
Optional. Number of runs of the EM algorithm. The solution providing the highest log-likelihood will be returned. If |
max_iter |
Optional. The maximum number of iterations for the EM algorithm. |
threshold |
Optional. A numeric value specifying the threshold for the relative difference of log-likelihood between two steps of the EM as stopping criteria. |
verbose |
Optional. A logical value indicating whether or not values of the log-likelihood should be printed during EM iterations. |
emMixHMM function implements the EM algorithm. This function starts
with an initialization of the parameters done by the method initParam
of
the class ParamMixHMM, then it alternates between the E-Step
(method of the class StatMixHMM) and the M-Step (method of
the class ParamMixHMM) until convergence (until the relative
variation of log-likelihood between two steps of the EM algorithm is less
than the threshold
parameter).
EM returns an object of class ModelMixHMM.
ModelMixHMM, ParamMixHMM, StatMixHMM
1 2 3 4 5 6 7 8 | data(toydataset)
Y <- t(toydataset[,2:ncol(toydataset)])
mixhmm <- emMixHMM(Y = Y, K = 3, R = 3, verbose = TRUE)
mixhmm$summary()
mixhmm$plot()
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