linearShrinkLWEst() computes an asymptotically optimal
convex combination of the sample covariance matrix and the identity matrix.
This convex combination effectively shrinks the eigenvalues of the sample
covariance matrix towards the identity. This estimator is more accurate
than the sample covariance matrix in high-dimensional settings under fairly
loose assumptions. For more information, consider reviewing the manuscript
by Ledoit and Wolf (2004)
.
linearShrinkLWEst(dat)A numeric data.frame, matrix, or similar object.
A matrix corresponding to the Ledoit-Wolf linear shrinkage
estimate of the covariance matrix.
Ledoit O, Wolf M (2004). “A well-conditioned estimator for large-dimensional covariance matrices.” Journal of Multivariate Analysis, 88(2), 365 - 411. ISSN 0047-259X, doi: 10.1016/S0047-259X(03)00096-4 , https://www.sciencedirect.com/science/article/pii/S0047259X03000964.
linearShrinkLWEst(dat = mtcars)
#> mpg cyl disp hp drat wt
#> mpg 91.994537 -8.8873959 -613.42707 -310.767007 2.12686354 -4.9577108
#> cyl -8.887396 59.8894321 193.45690 98.764472 -0.64760097 1.3248872
#> disp -613.427065 193.4568965 14940.34335 6512.334264 -45.60175405 104.3384878
#> hp -310.767007 98.7644717 6512.33426 4611.611408 -15.93997780 42.8196084
#> drat 2.126864 -0.6476010 -45.60175 -15.939978 57.07601236 -0.3611404
#> wt -4.957711 1.3248872 104.33849 42.819608 -0.36114040 57.7266467
#> qsec 4.369051 -1.8282308 -93.06738 -84.074160 0.08443329 -0.2959904
#> vs 1.954465 -0.7071628 -42.99882 -24.211536 0.11496280 -0.2651587
#> am 1.747884 -0.4512558 -35.42798 -8.062047 0.18424327 -0.3276000
#> gear 2.069330 -0.6290233 -49.22420 -6.161303 0.26741304 -0.4079978
#> carb -5.196475 1.4729303 76.61211 80.456377 -0.07597117 0.6547937
#> qsec vs am gear carb
#> mpg 4.36905127 1.95446525 1.74788384 2.06933037 -5.19647476
#> cyl -1.82823082 -0.70716284 -0.45125584 -0.62902330 1.47293033
#> disp -93.06738422 -42.99882145 -35.42797909 -49.22419861 76.61210740
#> hp -84.07415999 -24.21153649 -8.06204705 -6.16130273 80.45637743
#> drat 0.08443329 0.11496280 0.18424327 0.26741304 -0.07597117
#> wt -0.29599039 -0.26515872 -0.32760002 -0.40799779 0.65479372
#> qsec 59.89296866 0.64973028 -0.19859164 -0.27169118 -1.83526338
#> vs 0.64973028 57.04515281 0.04102326 0.07423256 -0.44930236
#> am -0.19859164 0.04102326 57.04026909 0.28325583 0.04493024
#> gear -0.27169118 0.07423256 0.28325583 57.32645515 0.31646514
#> carb -1.83526338 -0.44930236 0.04493024 0.31646514 59.32682738