StatMixRHLP-class {flamingos} | R Documentation

## A Reference Class which contains statistics of a mixture of RHLP models.

### Description

StatMixRHLP contains all the statistics associated to a MixRHLP model, in particular the E-Step (and C-Step) of the (C)EM algorithm.

### Fields

`pi_jkr`

  Array of size (nm, R, K) representing the logistic proportion for cluster k.

`tau_ik`

  Matrix of size (n, K) giving the posterior probabilities (fuzzy segmentation matrix) that the curve y_{i} originates from the k-th RHLP model.

`z_ik`

  Hard segmentation logical matrix of dimension (n, K) obtained by the Maximum a posteriori (MAP) rule: z_ik = 1 if z_i = arg max_k tau_ik; 0 otherwise.

`klas`

  Column matrix of the labels issued from `z_ik`. Its elements are klas[i] = z_i, i = 1,…,n.

`gamma_ijkr`

  Array of size (nm, R, K) giving the posterior probabilities that the observation y_{ij} originates from the r-th regime of the k-th RHLP model.

`polynomials`

  Array of size (m, R, K) giving the values of the estimated polynomial regression components.

`weighted_polynomials`

  Array of size (m, R, K) giving the values of the estimated polynomial regression components weighted by the prior probabilities `pi_jkr`.

`Ey`

  Matrix of size (m, K). `Ey` is the curve expectation (estimated signal): sum of the polynomial components weighted by the logistic probabilities `pi_jkr`.

`loglik`

  Numeric. Observed-data log-likelihood of the MixRHLP model.

`com_loglik`

  Numeric. Complete-data log-likelihood of the MixRHLP model.

`stored_loglik`

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

`stored_com_loglik`

  Numeric vector. Stored values of the Complete log-likelihood at each EM 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_fk_yij`

  Matrix of size (n, K) giving the values of the probability density function f(y_{i} | z_i = k, x, Ψ), i = 1,…,n.

`log_alphak_fk_yij`

  Matrix of size (n, K) giving the values of the logarithm of the joint probability density function f(y_{i}, z_{i} = k | x, Ψ), i = 1,…,n.

`log_gamma_ijkr`

  Array of size (nm, R, K) giving the logarithm of `gamma_ijkr`.

### Methods

`computeStats(paramMixRHLP)`

  Method used in the EM algorithm to compute statistics based on parameters provided by the object `paramMixRHLP` of class ParamMixRHLP.

`CStep(reg_irls)`

  Method used in the CEM algorithm to update statistics.

`EStep(paramMixRHLP)`

  Method used in the EM algorithm to update statistics based on parameters provided by the object `paramMixRHLP` of class ParamMixRHLP (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 if z_i = arg max_k tau_ik; 0 otherwise.

### See Also

ParamMixRHLP

[Package flamingos version 0.1.0 Index]