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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