Uses both labeled and unlabeled subsets of the data to obtain quick initial
estimates for mixture parameters and missingness mechanism parameters
(alpha, xi) via a warm-up EM procedure.
EM_FMM_SemiSupervised_Complete_Initial(
data,
g = 2,
ncov = 1,
alpha_init = 0.01,
warm_up_iter = 200,
tol = 1e-06
)A list with initial values for EM_FMM_SemiSupervised:
pi - mixture weights.
mu - list of component mean vectors.
Sigma - covariance matrix/matrices.
alpha - MCAR proportion.
xi - logistic regression coefficients for MAR mechanism.
A data frame containing:
The first p columns: numeric variables used in the FMM.
A column missing: indicator (0 = labeled, 1 = unlabeled/missing).
A column z: class labels for labeled rows (1:g);
NA for unlabeled.
Integer, number of mixture components (default 2).
Integer, covariance structure:
1 = shared (equal), 2 = class-specific (unequal).
Numeric in (0,1), initial MCAR proportion (default 0.01).
Integer, number of warm-up EM iterations (default 200).
Convergence tolerance on alpha (default 1e-6).
This function first calls initialestimate to get initial
\(\pi\), \(\mu\), \(\Sigma\).
Then it calls EM_FMM_SemiSupervised_Initial with these
values for a short warm-up run.
Covariance structure (equal vs. unequal) is determined
by ncov.