Find vertices of the set \(A(\vartheta, \bm{\pi})\), which characterizes link between finite mixture mean and regression function.
find.vertices.prob(mean, Pi, tol = 1e-08)find.vertices.nonneg(mean, Pi)
Parameter \(\vartheta\) of distribution.
Parameter \(\bm{\pi}\) of distribution.
A tolerance to determine if candidate vertices are distinct.
A \(J \times k\) matrix whose columns are the vertices of \(A(\vartheta, \bm{\pi})\).
For Mixture Link Binomial, the set \(A(\vartheta, \bm{\pi}) = \{ \bm{\mu} \in [0,1]^J : \bm{\mu}^T \bm{\pi} = \vartheta \}. \) For Mixture Link Poisson, the set \(A(\vartheta, \bm{\pi}) = \{ \bm{\mu} \in [0,\infty]^J : \bm{\mu}^T \bm{\pi} = \vartheta \}. \)
Andrew M. Raim, Nagaraj K. Neerchal, and Jorge G. Morel. An Extension of Generalized Linear Models to Finite Mixture Outcomes. arXiv preprint: 1612.03302