ParamMixHMMR contains all the parameters of a mixture of HMMR models.
fData
FData object representing the sample (covariates/inputs
X
and observed responses/outputs Y
).
K
The number of clusters (Number of HMMR models).
R
The number of regimes (HMMR components) for each cluster.
p
The order of the polynomial regression.
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.
beta
Parameters of the polynomial regressions. beta
is an array of
dimension (p + 1, R, K), with p
the order of the polynomial
regression. p
is fixed to 3 by default.
sigma2
The variances for the K
clusters. If MixHMMR model is
heteroskedastic (variance_type = "heteroskedastic"
) then sigma2
is a
matrix of size (R, K) (otherwise MixHMMR model is homoskedastic
(variance_type = "homoskedastic"
) and sigma2
is a matrix of size
nu
The degree of freedom of the MixHMMR model representing the complexity of the model.
phi
A list giving the regression design matrix for the polynomial regressions.
initParam(init_kmeans = TRUE, try_algo = 1)
Method to initialize parameters alpha
, prior
,
trans_mat
, beta
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 beta
and sigma2
are
initialized by segmenting the time series Y
uniformly into
R
contiguous segments. Otherwise, beta
and
sigma2
are initialized by segmenting randomly the time series
Y
into R
segments.
initRegressionParam(Y, k, R, phi, variance_type, try_algo)
Initialize beta
and sigma2
for the cluster k
.
MStep(statMixHMMR)
Method which implements the M-step of the EM algorithm to learn the
parameters of the MixHMMR model based on statistics provided by the
object statMixHMMR
of class StatMixHMMR (which contains
the E-step).
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