ParamSNMoE contains all the parameters of a SNMoE 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).
df
The degree of freedom of the SNMoE 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(statSNMoE, verbose_IRLS)
Method which implements the M-step of the EM algorithm to learn the
parameters of the SNMoE model based on statistics provided by the object
statSNMoE
of class StatSNMoE (which contains the E-step).
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.