ParamRHLP contains all the parameters of a RHLP 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},…,x_{m}*.`Y`

Numeric vector of length

*m*representing the observed response/output*y_{1},…,y_{m}*.`m`

Numeric. Length of the response/output vector

`Y`

.`K`

The number of regimes (RHLP components).

`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.`W`

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.`beta`

Parameters of the polynomial regressions.

*β = (β_{1},…,β_{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 RHLP model is heteroskedastic (`variance_type = "heteroskedastic"`

) then`sigma2`

is a matrix of size*(K, 1)*(otherwise RHLP model is homoskedastic (`variance_type = "homoskedastic"`

) and`sigma2`

is a matrix of size*(1, 1)*).`nu`

The degree of freedom of the RHLP 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

`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(statRHLP, verbose_IRLS)`

Method which implements the M-step of the EM algorithm to learn the parameters of the RHLP model based on statistics provided by the object

`statRHLP`

of class StatRHLP (which contains the E-step).

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