Collection of Robust Covariance and (Sparse) Precision Matrix Estimators.
robustcov
Robust covariance and precision matrix estimators. Based on the review of P.-L. Loh and X. L. Tan. (2018)
To install:
devtools::install_github("YunyiShen/robustcov")
There are in total 4 robust covariance and 3 correlation estimation implemented, namely:
corSpearman
: Spearman correlationcorKendall
: Kendall's taucorQuadrant
: Quadrant correlation coefficientscovGKmat
: Gnanadesikan-Kettenring estimator by Tarr et al. (2015) and Oellerer and Croux (2015)covSpearmanU
: SpearmanU covariance estimator by P.-L. Loh and X. L. Tan. (2018), The pairwise covariance matrix estimator proposed in Oellerer and Croux (2015), where the MAD estimator is combined with Spearman’s rhocovOGK
: Orthogonalized Gnanadesikan-Kettenring (OGK) estimator by Maronna, R. A. and Zamar, R. H. (2002)covNPD
: Nearest Positive (semi)-Definite projection of the pairwise covariance matrix estimator considered in Tarr et al. (2015).
P.-L. Loh and X. L. Tan. (2018) then used these robust estimates in Graphical Lasso (package glasso
) or Quadratic Approximation (package QUIC
) to obtain sparse solutions to precision matrix
With glasso
, a function robglasso
stand for robust graphical LASSO is implemented. It has build in cross validation described in P.-L. Loh and X. L. Tan. (2018), for instance, to use the method with cross validation:
robglasso(data=matrix(rnorm(100),20,5), covest = cov,CV=TRUE)
Where data
should be a matrix and covest
should be a function that estimate the covariance e.g. anyone mentioned above. The result list contains everything from glasso
output with the optimal tuning parameter found by cross validation. One can also decide fold by setting fold
in robglasso
. For more details see ?robglasso
.