ParamMHMMR-class: A Reference Class which contains parameters of a MHMMR model.

Description Fields Methods


ParamMHMMR contains all the parameters of a MHMMR model. The parameters are calculated by the initialization Method and then updated by the Method implementing the M-Step of the EM algorithm.



MData object representing the sample (covariates/inputs X and observed multivariate responses/outputs Y).


The number of regimes (MHMMR components).


The order of the polynomial regression.


Character indicating if the model is homoskedastic (variance_type = "homoskedastic") or heteroskedastic (variance_type = "heteroskedastic"). By default the model is heteroskedastic.


The prior probabilities of the Markov chain. prior is a row matrix of dimension (1, K).


The transition matrix of the Markov chain. trans_mat is a matrix of dimension (K, K).


Mask applied to the transition matrices trans_mat. By default, a mask of order one is applied.


Parameters of the polynomial regressions. β = (β_{1},…,β_{K}) is an array of dimension (p + 1, d, K), with p the order of the polynomial regression. p is fixed to 3 by default.


The variances for the K regimes. If MRHLP model is heteroskedastic (variance_type = "heteroskedastic") then sigma2 is an array of size (d, d, K) (otherwise MRHLP model is homoskedastic (variance_type = "homoskedastic") and sigma2 is a matrix of size (d, d)).


The degree of freedom of the MHMMR model representing the complexity of the model.


A list giving the regression design matrices for the polynomial and the logistic regressions.


initParam(try_algo = 1)

Method to initialize parameters 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.


Method which implements the M-step of the EM algorithm to learn the parameters of the MHMMR model based on statistics provided by the object statMHMMR of class StatMHMMR (which contains the E-step).

samurais documentation built on July 28, 2019, 5:02 p.m.