ParamStMoE contains all the parameters of a StMoE model.

`X`

Numeric vector of length

*n*representing the covariates/inputs*x_{1},…,x_{n}*.`Y`

Numeric vector of length

*n*representing the observed response/output*y_{1},…,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.

*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.`beta`

Polynomial 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.`sigma2`

The variances for the

`K`

mixture components (matrix of size*(1, K)*).`lambda`

The skewness parameters for each experts (matrix of size

*(1, K)*).`delta`

delta is equal to

*δ = λ / (1+λ^2)^(1/2)*.`nu`

The degree of freedom for the Student distribution for each experts (matrix of size

*(1, K)*).`df`

The 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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