Full log-likelihood function with both terms of ignoring and missing
loglk_full(dat, zm, pi, mu, sigma, xi)Log-likelihood value
An \(n\times p\) matrix where each row represents an individual observation
An n-dimensional vector containing the class labels including the missing-label denoted as NA.
A g-dimensional vector for the initial values of the mixing proportions.
A \(p \times g\) matrix for the initial values of the location parameters.
A \(p\times p\) covariance matrix,or a list of g covariance matrices with dimension \(p\times p \times g\).
It is assumed to fit the model with a common covariance matrix if sigma is a \(p\times p\) covariance matrix;
otherwise it is assumed to fit the model with unequal covariance matrices.
A 2-dimensional vector containing the initial values of the coefficients in the logistic function of the Shannon entropy.
The full log-likelihood function can be expressed as $$ \log L_{PC}^{({full})}(\boldsymbol{\Psi})=\log L_{PC}^{({ig})}(\theta)+\log L_{PC}^{({miss})}(\theta,\boldsymbol{\xi}),$$ where\(\log L_{PC}^{({ig})}(\theta)\)is the log likelihood function formed ignoring the missing in the label of the unclassified features, and \(\log L_{PC}^{({miss})}(\theta,\boldsymbol{\xi})\) is the log likelihood function formed on the basis of the missing-label indicator.