sgt | R Documentation |
Estimates a sparse inverse covariance matrix using the closed form solution of graphical lasso under acyclic graph structure.
sgt(x, lambda, size = NULL)
x |
There are 2 options: (1) |
lambda |
The regularization parameter for graphical lasso. |
size |
A non-negative integer for determining the model size, i.e., the number of non-zero off-diagonal entries in the upper-triangular precision matrix,
which is also the number of edges in the graph. |
Soft Graphical Thresholding (SGT) algorithm proceeds by thresholding the sample covariance matrix and estimating the inverse covariance matrix with a closed-form formula. If the graph structure detected by the thresholding procedure is acyclic, then the estimation is equivalent to the solution of graphical lasso.
A list with following components:
Omega |
Estimated inverse covariance matrix. |
active.entry |
The position of the non-zero entries of |
is.acyclic |
The boolean flag of whether the detected graph structure is acyclic or not. |
Either lambda
or size
should specified when function sgt
is called.
If both arguments are given, only lambda
would be considered.
Fattahi, Salar, and Somayeh Sojoudi. Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions. Journal of Machine Learning Research 20.10 (2019): 1-44. doi: 10.5555/3322706.3322716
library(gif)
data("ar1")
res <- sgt(ar1[["x"]], lambda = 0.01)
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