ParamMixHMMR-class {flamingos} | R Documentation

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

### Description

ParamMixHMMR contains all the parameters of a mixture of HMMR models.

### Fields

`fData`

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

`K`

  The number of clusters (Number of HMMR models).

`R`

  The number of regimes (HMMR components) for each cluster.

`p`

  The order of the polynomial regression.

`variance_type`

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

`order_constraint`

  A logical indicating whether or not a mask of order one should be applied to the transition matrix of the Markov chain to provide ordered states. For the purpose of segmentation, it must be set to `TRUE` (which is the default value).

`alpha`

  Cluster weights. Matrix of dimension (K, 1).

`prior`

  The prior probabilities of the Markov chains. `prior` is a matrix of dimension (R, K). The k-th column represents the prior distribution of the Markov chain asociated to the cluster k.

`trans_mat`

  The transition matrices of the Markov chains. `trans_mat` is an array of dimension (R, R, K).

`mask`

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

`beta`

  Parameters of the polynomial regressions. `beta` is an array of dimension (p + 1, R, K), with `p` the order of the polynomial regression. `p` is fixed to 3 by default.

`sigma2`

  The variances for the `K` clusters. If MixHMMR model is heteroskedastic (`variance_type = "heteroskedastic"`) then `sigma2` is a matrix of size (R, K) (otherwise MixHMMR model is homoskedastic (`variance_type = "homoskedastic"`) and `sigma2` is a matrix of size

`nu`

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

`phi`

  A list giving the regression design matrix for the polynomial regressions.

### Methods

`initParam(init_kmeans = TRUE, try_algo = 1)`

  Method to initialize parameters `alpha`, `prior`, `trans_mat`, `beta` and `sigma2`.

  If `init_kmeans = TRUE` then the curve partition is initialized by the K-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, `beta` and `sigma2` are initialized by segmenting randomly the time series `Y` into `R` segments.

`initRegressionParam(Y, k, R, phi, variance_type, try_algo)`

  Initialize `beta` and `sigma2` for the cluster `k`.

`MStep(statMixHMMR)`

  Method which implements the M-step of the EM algorithm to learn the parameters of the MixHMMR model based on statistics provided by the object `statMixHMMR` of class StatMixHMMR (which contains the E-step).

[Package flamingos version 0.1.0 Index]