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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