StatStMoE-class {meteorits} | R Documentation |
A Reference Class which contains statistics of a StMoE model.
Description
StatStMoE contains all the statistics associated to a StMoE model. It mainly includes the E-Step of the ECM algorithm calculating the posterior distribution of the hidden variables, as well as the calculation of the log-likelhood.
Fields
piik
Matrix of size
representing the probabilities
of the latent variable
.
z_ik
Hard segmentation logical matrix of dimension
obtained by the Maximum a posteriori (MAP) rule:
,
.
klas
Column matrix of the labels issued from
z_ik
. Its elements are,
.
tik
Matrix of size
giving the posterior probability
that the observation
originates from the
-th expert.
Ey_k
Matrix of dimension (n, K) giving the estimated means of the experts.
Ey
Column matrix of dimension n giving the estimated mean of the StMoE.
Var_yk
Column matrix of dimension K giving the estimated means of the experts.
Vary
Column matrix of dimension n giving the estimated variance of the response.
loglik
Numeric. Observed-data log-likelihood of the StMoE model.
com_loglik
Numeric. Complete-data log-likelihood of the StMoE model.
stored_loglik
Numeric vector. Stored values of the log-likelihood at each ECM iteration.
BIC
Numeric. Value of BIC (Bayesian Information Criterion).
ICL
Numeric. Value of ICL (Integrated Completed Likelihood).
AIC
Numeric. Value of AIC (Akaike Information Criterion).
log_piik_fik
Matrix of size
giving the values of the logarithm of the joint probability
,
.
log_sum_piik_fik
Column matrix of size m giving the values of
,
.
dik
It represents the value of
.
wik
Conditional expectations
.
E1ik
Conditional expectations
.
E2ik
Conditional expectations
.
E3ik
Conditional expectations
.
stme_pdf
Skew-t mixture of experts density.
Methods
computeLikelihood(reg_irls)
Method to compute the log-likelihood.
reg_irls
is the value of the regularization part in the IRLS algorithm.computeStats(paramStMoE)
Method used in the ECM algorithm to compute statistics based on parameters provided by the object
paramStMoE
of class ParamStMoE.EStep(paramStMoE, calcTau = FALSE, calcE1 = FALSE, calcE2 = FALSE, calcE3 = FALSE)
Method used in the ECM algorithm to update statistics based on parameters provided by the object
paramStMoE
of class ParamStMoE (prior and posterior probabilities).MAP()
MAP calculates values of the fields
z_ik
andklas
by applying the Maximum A Posteriori Bayes allocation rule.