ParamHMMR-class {samurais} | R Documentation |
ParamHMMR contains all the parameters of a HMMR model. The parameters are calculated by the initialization Method and then updated by the Method implementing the M-Step of the EM algorithm.
X
Numeric vector of length m representing the covariates/inputs
x_{1},\dots,x_{m}
.
Y
Numeric vector of length m representing the observed
response/output y_{1},\dots,y_{m}
.
m
Numeric. Length of the response/output vector Y
.
K
The number of regimes (HMMR components).
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.
prior
The prior probabilities of the Markov chain. prior
is a row
matrix of dimension (1, K)
.
trans_mat
The transition matrix of the Markov chain. trans_mat
is a
matrix of dimension (K, 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. \boldsymbol{\beta}
= (\boldsymbol{\beta}_{1},\dots,\boldsymbol{\beta}_{K})
is a matrix of dimension (p + 1, K)
,
with p
the order of the polynomial regression. p
is fixed to 3 by
default.
sigma2
The variances for the K
regimes. If HMMR model is
heteroskedastic (variance_type = "heteroskedastic"
) then sigma2
is a
matrix of size (K, 1)
(otherwise HMMR model is homoskedastic
(variance_type = "homoskedastic"
) and sigma2
is a matrix of size
(1, 1)
).
nu
The degree of freedom of the HMMR model representing the complexity of the model.
phi
A list giving the regression design matrices for the polynomial and the logistic regressions.
initParam(try_algo = 1)
Method to initialize parameters mask
, prior
,
trans_mat
, beta
and sigma2
.
If try_algo = 1
then beta
and sigma2
are
initialized by segmenting the time series Y
uniformly into
K
contiguous segments. Otherwise, beta
and
sigma2
are initialized by segmenting randomly the time series
Y
into K
segments.
MStep(statHMMR)
Method which implements the M-step of the EM algorithm to learn the
parameters of the HMMR model based on statistics provided by the object
statHMMR
of class StatHMMR (which contains the E-step).