Description Usage Arguments Details Value Author(s) References
Penalized precision matrix estimation using the graphical lasso (glasso) algorithm
1 2 3 | GLASSOc(S, initSigma, initOmega, lam, crit_out = "avg", crit_in = "loss",
tol_out = 1e-04, tol_in = 1e-04, maxit_out = 10000L,
maxit_in = 10000L)
|
S |
pxp sample covariance matrix (denominator n). |
initSigma |
initialization matrix for estimated covariance matrix Sigma |
initOmega |
initialization matrix for Omega used to initialize the Betas |
lam |
tuning parameter for lasso penalty. |
crit_out |
criterion for convergence in outer (blockwise) loop. Criterion |
crit_in |
criterion for convergence in inner (lasso) loop. Criterion for convergence. Criterion |
tol_out |
convergence tolerance for outer (blockwise) loop. Defaults to 1e-4. |
tol_in |
convergence tolerance for inner (lasso) loop. Defaults to 1e-4. |
maxit_out |
maximum number of iterations for outer (blockwise) loop. Defaults to 1e4. |
maxit_in |
maximum number of iterations for inner (lasso) loop. Defaults to 1e4. |
For details on the implementation of 'GLASSOO', see the vignette https://mgallow.github.io/GLASSOO/.
returns list of returns which includes:
Iterations |
number of iterations. |
lam |
optimal tuning parameters. |
Omega |
estimated penalized precision matrix. |
Sigma |
estimated covariance matrix. |
Matt Galloway gall0441@umn.edu
Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 'Sparse inverse covariance estimation with the graphical lasso.' Biostatistics 9.3 (2008): 432-441.
Banerjee, Onureen, Ghauoui, Laurent El, and d'Aspremont, Alexandre. 2008. "Model Selection through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data." Journal of Machine Learning Research 9: 485-516.
Tibshirani, Robert. 1996. "Regression Shrinkage and Selection via the Lasso." Journal of the Royal Statistical Society. Series B (Methodological). JSTOR: 267-288.
Meinshausen, Nicolai and Buhlmann, Peter. 2006. "High-Dimensional Graphs and Variable Selection with the Lasso." The Annals of Statistics. JSTOR: 1436-1462.
Witten, Daniela M, Friedman, Jerome H, and Simon, Noah. 2011. "New Insights and Faster computations for the Graphical Lasso." Journal of Computation and Graphical Statistics. Taylor and Francis: 892-900.
Tibshirani, Robert, Bien, Jacob, Friedman, Jerome, Hastie, Trevor, Simon, Noah, Jonathan, Taylor, and Tibshirani, Ryan J. "Strong Rules for Discarding Predictors in Lasso-Type Problems." Journal of the Royal Statistical Society: Series B (Statistical Methodology). Wiley Online Library 74 (2): 245-266.
Ghaoui, Laurent El, Viallon, Vivian, and Rabbani, Tarek. 2010. "Safe Feature Elimination for the Lasso and Sparse Supervised Learning Problems." arXiv preprint arXiv: 1009.4219.
Osborne, Michael R, Presnell, Brett, and Turlach, Berwin A. "On the Lasso and its Dual." Journal of Computational and Graphical Statistics. Taylor and Francis 9 (2): 319-337.
Rothman, Adam. 2017. "STAT 8931 notes on an algorithm to compute the Lasso-penalized Gausssian likelihood precision matrix estimator."
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