flamingos (version 0.1.0)

StatMixHMM-class: A Reference Class which contains statistics of a mixture of HMM model.

Description

StatMixHMM contains all the statistics associated to a MixHMM model, in particular the E-Step of the EM algorithm.

Fields

tau_ik

Matrix of size \((n, K)\) giving the posterior probabilities that the curve \(\boldsymbol{y}_{i}\) originates from the \(k\)-th HMM model.

gamma_ikjr

Array of size \((nm, R, K)\) giving the posterior probabilities that the observation \(\boldsymbol{y}_{ij}\) originates from the \(r\)-th regime of the \(k\)-th HMM model.

loglik

Numeric. Log-likelihood of the MixHMM model.

stored_loglik

Numeric vector. Stored values of the log-likelihood at each iteration of the EM algorithm.

klas

Row matrix of the labels issued from tau_ik. Its elements are \(klas[i] = z\_i\), \(i = 1,\dots,n\).

z_ik

Hard segmentation logical matrix of dimension \((n, K)\) obtained by the Maximum a posteriori (MAP) rule: \(z\_ik = 1 \ \textrm{if} \ z\_i = \textrm{arg} \ \textrm{max}_{k} \ P(z_{ik} = 1 | \boldsymbol{y}_{i}; \boldsymbol{\Psi}) = tau\_tk;\ 0 \ \textrm{otherwise}\).

smoothed

Matrix of size \((m, K)\) giving the smoothed time series. The smoothed time series are computed by combining the time series \(\boldsymbol{y}_{i}\) with both the estimated posterior regime probabilities gamma_ikjr and the corresponding estimated posterior cluster probability tau_ik. The k-th column gives the estimated mean series of cluster k.

BIC

Numeric. Value of BIC (Bayesian Information Criterion).

AIC

Numeric. Value of AIC (Akaike Information Criterion).

ICL1

Numeric. Value of ICL (Integrated Completed Likelihood Criterion).

log_alpha_k_fyi

Private. Only defined for calculations.

exp_num_trans

Private. Only defined for calculations.

exp_num_trans_from_l

Private. Only defined for calculations.

Methods

computeStats(paramMixHMM)

Method used in the EM algorithm to compute statistics based on parameters provided by the object paramMixHMM of class ParamMixHMM.

EStep(paramMixHMM)

Method used in the EM algorithm to update statistics based on parameters provided by the object paramMixHMM of class ParamMixHMM (prior and posterior probabilities).

MAP()

MAP calculates values of the fields z_ik and klas by applying the Maximum A Posteriori Bayes allocation rule.

\(z\_ik = 1 \ \textrm{if} \ z\_i = \textrm{arg} \ \textrm{max}_{k} \ P(z_{ik} = 1 | \boldsymbol{y}_{i}; \boldsymbol{\Psi}) = tau\_tk;\ 0 \ \textrm{otherwise}\).

See Also

ParamMixHMM