ParamMixRHLP contains all the parameters of a mixture of RHLP models.
fData
FData object representing the sample (covariates/inputs
X
and observed responses/outputs Y
).
K
The number of clusters (Number of RHLP models).
R
The number of regimes (RHLP components) for each cluster.
p
The order of the polynomial regression.
q
The dimension of the logistic regression. For the purpose of segmentation, it must be set to 1.
variance_type
Character indicating if the model is homoskedastic
(variance_type = "homoskedastic"
) or heteroskedastic (variance_type = "heteroskedastic"
). By default the model is heteroskedastic.
alpha
Cluster weights. Matrix of dimension (1, K).
W
Parameters of the logistic process. W = (w_{1},…,w_{K}) is
an array of dimension (q + 1, R - 1, K), with w_{k} =
(w_{k,1},…,w_{k,R-1}), k = 1,…,K, and q
the order of the
logistic regression. q
is fixed to 1 by default.
beta
Parameters of the polynomial regressions. β =
(β_{1},…,β_{K}) is an array of dimension (p + 1, R, K),
with β_{k} =
(β_{k,1},…,β_{k,R}), k = 1,…,K, p
the order of the
polynomial regression. p
is fixed to 3 by default.
sigma2
The variances for the K
clusters. If MixRHLP model is
heteroskedastic (variance_type = "heteroskedastic"
) then sigma2
is a
matrix of size (R, K) (otherwise MixRHLP model is homoskedastic
(variance_type = "homoskedastic"
) and sigma2
is a matrix of size
(K, 1)).
nu
The degree of freedom of the MixRHLP model representing the complexity of the model.
phi
A list giving the regression design matrices for the polynomial and the logistic regressions.
CMStep(statMixRHLP, verbose_IRLS = FALSE)
Method which implements the M-step of the CEM algorithm to learn the
parameters of the MixRHLP model based on statistics provided by the
object statMixRHLP
of class StatMixRHLP (which contains
the E-step and the C-step).
initParam(init_kmeans = TRUE, try_algo = 1)
Method to initialize parameters alpha
, W
, beta
and sigma2
.
If init_kmeans = TRUE
then the curve partition is initialized by
the R-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, W
, beta
and
sigma2
are initialized by segmenting randomly the time series
Y
into R
segments.
initRegressionParam(Yk, k, try_algo = 1)
Initialize the matrix of polynomial regression coefficients beta_k for
the cluster k
.
MStep(statMixRHLP, verbose_IRLS = FALSE)
Method which implements the M-step of the EM algorithm to learn the
parameters of the MixRHLP model based on statistics provided by the
object statMixRHLP
of class StatMixRHLP (which contains
the E-step).
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