Adj2Lap

graph adjacency matrix (squared symmetric matrix)

Compute graph Laplacian matrix from graph adjacency matrix

Provides graph-constrained regression methods in which
regularization parameters are selected automatically via estimation of
equivalent Linear Mixed Model formulation. 'riPEER' (ridgified Partially
Empirical Eigenvectors for Regression) method employs a penalty term being
a linear combination of graph-originated and ridge-originated penalty terms,
whose two regularization parameters are ML estimators from corresponding
Linear Mixed Model solution; a graph-originated penalty term allows imposing
similarity between coefficients based on graph information given whereas
additional ridge-originated penalty term facilitates parameters estimation:
it reduces computational issues arising from singularity in a graph-originated
penalty matrix and yields plausible results in situations when graph information
is not informative. 'riPEERc' (ridgified Partially Empirical Eigenvectors
for Regression with constant) method utilizes addition of a diagonal matrix
multiplied by a predefined (small) scalar to handle the non-invertibility of
a graph Laplacian matrix. 'vrPEER' (variable reducted PEER) method performs
variable-reduction procedure to handle the non-invertibility of a graph
Laplacian matrix.

Adj2Lap: Compute graph Laplacian matrix from graph adjacency matrix

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