ParamMixRHLP-class {flamingos} | R Documentation

## A Reference Class which contains parameters of a mixture of RHLP models.

### Description

ParamMixRHLP contains all the parameters of a mixture of RHLP models.

### Fields

`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.

### Methods

`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).

[Package flamingos version 0.1.0 Index]