ParamMixHMM contains all the parameters of a mixture of HMM models.
fData
FData object representing the sample (covariates/inputs
X
and observed responses/outputs Y
).
K
The number of clusters (Number of HMM models).
R
The number of regimes (HMM components) for each cluster.
variance_type
Character indicating if the model is homoskedastic
(variance_type = "homoskedastic"
) or heteroskedastic (variance_type = "heteroskedastic"
). By default the model is heteroskedastic.
order_constraint
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
TRUE
(which is the default value).
alpha
Cluster weights. Matrix of dimension (K, 1).
prior
The prior probabilities of the Markov chains. prior
is a
matrix of dimension (R, K). The k-th column represents the prior
distribution of the Markov chain asociated to the cluster k.
trans_mat
The transition matrices of the Markov chains. trans_mat
is
an array of dimension (R, R, K).
mask
Mask applied to the transition matrices trans_mat
. By default,
a mask of order one is applied.
mu
Means. Matrix of dimension (R, K). The k-th column gives
represents the k-th cluster and gives the means for the R
regimes.
sigma2
The variances for the K
clusters. If MixHMM model is
heteroskedastic (variance_type = "heteroskedastic"
) then sigma2
is a
matrix of size (R, K) (otherwise MixHMM model is homoskedastic
(variance_type = "homoskedastic"
) and sigma2
is a matrix of size
(1, K)).
nu
The degrees of freedom of the MixHMM model representing the complexity of the model.
initGaussParamHmm(Y, k, R, variance_type, try_algo)
Initialize the means mu
and sigma2
for the cluster
k
.
initParam(init_kmeans = TRUE, try_algo = 1)
Method to initialize parameters alpha
, prior
, trans_mat
,
mu
and sigma2
.
If init_kmeans = TRUE
then the curve partition is initialized by
the K-means algorithm. Otherwise the curve partition is initialized
randomly.
If try_algo = 1
then mu
and sigma2
are
initialized by segmenting the time series Y
uniformly into
R
contiguous segments. Otherwise, mu
and
sigma2
are initialized by segmenting randomly the time series
Y
into R
segments.
MStep(statMixHMM)
Method which implements the M-step of the EM algorithm to learn the
parameters of the MixHMM model based on statistics provided by the object
statMixHMM
of class StatMixHMM (which contains the
E-step).
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