StatMixHMMR-class {flamingos} | R Documentation |
A Reference Class which contains statistics of a mixture of HMMR models.
Description
StatMixHMMR contains all the statistics associated to a MixHMMR 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 thek
-th HMMR model.gamma_ikjr
Array of size
(nm, R, K)
giving the posterior probabilities that the observation\boldsymbol{y}_{ij}
originates from ther
-th regime of thek
-th HMM model.loglik
Numeric. Log-likelihood of the MixHMMR 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 areklas[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\_ik;\ 0 \ \textrm{otherwise}
.smoothed
Matrix of size
(m, K)
giving the smoothed time series. The smoothed time series are computed by combining the polynomial regression components with both the estimated posterior regime probabilitiesgamma_ikjr
and the corresponding estimated posterior cluster probabilitytau_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(paramMixHMMR)
Method used in the EM algorithm to compute statistics based on parameters provided by the object
paramMixHMMR
of class ParamMixHMMR.EStep(paramMixHMMR)
Method used in the EM algorithm to update statistics based on parameters provided by the object
paramMixHMMR
of class ParamMixHMMR (prior and posterior probabilities).MAP()
MAP calculates values of the fields
z_ik
andklas
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\_ik;\ 0 \ \textrm{otherwise}
.