ParamMixHMMR-class function

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

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

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).