ParamTMoE-class {meteorits}

A Reference Class which contains parameters of a TMoE model.

Description

ParamTMoE contains all the parameters of a TMoE model.

Fields

X

Numeric vector of length n representing the covariates/inputs x_{1},\dots,x_{n}.

Y

Numeric vector of length n representing the observed response/output y_{1},\dots,y_{n}.

n

Numeric. Length of the response/output vector Y.

K

The number of experts.

p

The order of the polynomial regression for the experts.

q

The order of the logistic regression for the gating network.

alpha

Parameters of the gating network. \boldsymbol{\alpha} = (\boldsymbol{\alpha}_{1},\dots,\boldsymbol{\alpha}_{K-1}) is a matrix of dimension (q + 1, K - 1), with q the order of the logistic regression for the gating network. q is fixed to 1 by default.

beta

Polynomial regressions coefficients for each expert. \boldsymbol{\beta} = (\boldsymbol{\beta}_{1},\dots,\boldsymbol{\beta}_{K}) is a matrix of dimension (p + 1, K), with p the order of the polynomial regression. p is fixed to 3 by default.

sigma2

The variances for the K mixture components (matrix of size (1, K)).

nu

The degree of freedom for the Student distribution for each experts (matrix of size (1, K)).

df

The degree of freedom of the TMoE model representing the complexity of the model.

Methods

initParam(segmental = FALSE)

Method to initialize parameters alpha, beta and sigma2.

If segmental = TRUE then alpha, beta and sigma2 are initialized by clustering the response Y uniformly into K contiguous segments. Otherwise, alpha, beta and sigma2 are initialized by clustering randomly the response Y into K segments.

MStep(statTMoE, verbose_IRLS)

Method which implements the M-step of the EM algorithm to learn the parameters of the TMoE model based on statistics provided by the object statTMoE of class StatTMoE (which contains the E-step).