Description Usage Arguments Value Author(s) References See Also Examples
Robust estimation of sparse inverse covariance matrix via the gamma-divergence
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x |
A data matrix. |
gamma |
(non-negative) A numeric vector of tuning parameters for the gamma-divergence. |
lambda |
(Optional) A matrix of tuning parameters for the lasso. Each column corresponds to |
nlambda |
The length of lambda if |
delta |
The ratio of maximum value of lambda and the minimum value of lambda if |
penalty.offdiag |
If |
method |
Estimation method of the graphical lasso. Default is |
maxit |
The maximum number of iteration. |
tol.plogL |
Tolerance of the maximum value of penalized likelihood estimation. |
msg |
If |
Omega.init |
(Optional) The initial value of the inverse covariance matrix. |
mu.init |
(Optional) The initial value of the mean vector. |
Omega |
inverse covariance matrix |
mu |
mean vector |
weight |
weight obtained by the gamma-lasso algorithm |
nedges |
number of edges |
lambda |
lambda |
gamma |
gamma |
Kei Hirose
mail@keihirose.com
Hirose, K. and Fujisawa, H. (2017).
Robust sparse Gaussian graphical modeling, Journal of Multivariate Analysis, 161, 172-190.
Fujisawa, H., and Eguchi, S. (2008).
Robust parameter estimation with a small bias against heavy contamination, Journal of Multivariate Analysis, 99(9), 2053-2081.
out
object
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