ParamStMoE contains all the parameters of a StMoE model.
XNumeric vector of length n representing the covariates/inputs x_{1},…,x_{n}.
YNumeric vector of length n representing the observed response/output y_{1},…,y_{n}.
nNumeric. Length of the response/output vector Y.
KThe number of experts.
pThe order of the polynomial regression for the experts.
qThe order of the logistic regression for the gating network.
alphaParameters of the gating network. α =
(α_{1},…,α_{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.
betaPolynomial regressions coefficients for each expert.
β =
(β_{1},…,β_{K}) is a matrix of dimension (p + 1, K),
with p the order of the polynomial regression. p is fixed to 3 by
default.
sigma2The variances for the K mixture components (matrix of size
(1, K)).
lambdaThe skewness parameters for each experts (matrix of size (1, K)).
deltadelta is equal to δ = λ / (1+λ^2)^(1/2).
nuThe degree of freedom for the Student distribution for each experts (matrix of size (1, K)).
dfThe degree of freedom of the StMoE model representing the complexity of the model.
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(statStMoE, calcAlpha = FALSE, calcBeta = FALSE, calcSigma2 = FALSE,
calcLambda = FALSE, calcNu = FALSE, verbose_IRLS = FALSE)Method which implements the M-step of the EM algorithm to learn the
parameters of the StMoE model based on statistics provided by the object
statStMoE of class StatStMoE (which contains the E-step).
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