zoomJGL {zoomJGL} | R Documentation |
Estimate jointly multiple graphs that resemble each other.
zoomJGL(Y,lambda1,lambda2,list.controls)
Y |
List of data matrices where each element of the list is a matrix of dimension Txp, with p increasing across scales. |
lambda1 |
Regularization parameter that dictates the sparsity of the undirected graphs |
lambda2 |
Regularization parameter that dictates the sparsity of the directed graphs |
list.controls |
List of control parameters specifying
|
A list containing the objects:
Theta |
List of parameter matrices of size pxp at each scale. The parameters correspond to undirected edges in the graphs. |
Gamma |
List of parameter matrices of size pxp at each scale. The parameters correspond to directed edges in the graphs. |
Undirected |
List of adjacency matrices of size pxp at each scale corresponding to the undirected graphs. |
Directed |
List of adjacency matrices of size pxp at each scale corresponding to the undirected graphs. |
Constraint1 |
List of matrices that map lambda1 to enforce sparsity and a similar structure across scales for the undirected graphs. |
Constraint2 |
List of matrices that map lambda1 to enforce sparsity and a similar structure across scales for the undirected graphs. |
Convergence |
Convergence performance at each iteration of the algorithm. The values for DiffObjective, DiffZTheta, DiffGamma which capture the difference between two consecutive iterations for the objective function, the matrices Z-Theta and Gamma should get closer to 0 across iterations. |
IC |
Information criteria scores: AIC, BIC, eBIC(.1), eBIC(.5), eBIC(.9) |
data(fmri) obj=zoomJGL(Y,.4,.2)