MGRAF2 {CISE} | R Documentation |
MGRAF2
returns the estimated common structure Z and Λ that
are shared by all the subjects as well as the subject-specific low rank
matrix Q_i for multiple undirected graphs.
MGRAF2(A, K, tol, maxit)
A |
Binary array with size VxVxn storing the VxV symmetric adjacency matrices of n graphs. |
K |
An integer that specifies the latent dimension of the graphs |
tol |
A numeric scalar that specifies the convergence threshold of CISE algorithm. CISE iteration continues until the absolute percent change in joint log-likelihood is smaller than this value. Default is tol = 0.01. |
maxit |
An integer that specifies the maximum number of iterations. Default is maxit = 5. |
The subject-specific deviation D_i is decomposed into
D_i = Q_i * Λ * Q_i^{\top},
where each Q_i is a VxK orthonormal matrix and Λ is a KxK diagonal matrix.
A list is returned containing the ingredients below from M-GRAF2 model corresponding to the largest log-likelihood over iterations.
Z |
A numeric vector containing the lower triangular entries in the estimated matrix Z. |
Lambda |
Kx1 vector storing the diagonal entries in Λ. |
Q |
VxKxn array containing the estimated VxK orthonormal matrix Q_i, i=1,...,n. |
D_LT |
Lxn matrix where each column stores the lower triangular entries in D_i = Q_i * Λ * Q_i^{\top}; L=V(V-1)/2. |
LL_max |
Maximum log-likelihood across iterations. |
LL |
Joint log-likelihood at each iteration. |
data(A) n = dim(A)[3] subs = sample.int(n=n,size=30) A_sub = A[ , , subs] res = MGRAF2(A=A_sub, K=3, tol=0.01, maxit=5)