ParamMRHLP-class: A Reference Class which contains the parameters of a MRHLP...

Description Fields Methods


ParamMRHLP contains all the parameters of a MRHLP 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 responses/outputs Y).


The number of regimes (MRHLP components).


The order of the polynomial regression.


The dimension of the logistic regression. For the purpose of segmentation, it must be set to 1.


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


Parameters of the logistic process. W = (w_{1},…,w_{K-1}) is a matrix of dimension (q + 1, K - 1), with q the order of the logistic regression. q is fixed to 1 by default.


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 MRHLP 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 W, 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, W, beta and sigma2 are initialized by segmenting randomly the time series Y into K segments.

MStep(statMRHLP, verbose_IRLS)

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

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