Description Usage Arguments Details See Also Examples
This package implements the two methods introduced in Bien, J. (2016) "Graph-Guided Banding of the Covariance Matrix" http://arxiv.org/abs/1606.00451
Computes the global or local GGB estimator (depending on type
argument) along a grid of lambda values.
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S |
p-by-p sample covariance matrix |
g |
seed graph (not necessarily connected) |
type |
either "global" or "local" |
lambda |
positive tuning parameter(s) |
delta |
lower bound on eigenvalues (default, NULL, means no bound) |
flmin |
ratio of smallest to largest lambda value (ignored if
|
nlam |
number of lambda values (ignored if |
max_depths |
maximal bandwidth(s) considered. If type is "global", this must be a single non-negative integer. If type is "local", this must be a p-vector of non-negative integers. Default is NULL, which is equivalent to taking max_depths to be >= diameter(g). (These are referred to as M and M_j in the paper.) |
out_iter |
number of iterations of outer loop (i.e., # of eigenvalue
decompositions). Ignored when |
in_iter |
number of iterations on inner loop (i.e., # cycles over
row/cols) when |
out_tol |
convergence threshold for outer loop BCD (ignored when
|
in_tol |
convergence threshold for inner loop BCD when |
verbose |
level of verbosity in printed output (3, 2, 1, 0) |
The main function is ggb
.
To evaluate the proximal operator, it uses the "closed form" of Yan and Bien (2015) for the global GGB estimator as implemented in the R package hsm and uses blockwise coordinate descent for the local GGB estimator.
If delta
is non-NULL, then alternates between evaluating proximal
operator and projecting eigenvalues.
The weights used in this penalty are the square root of the group size.
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